diff --git a/.github/workflows/test-master.yml b/.github/workflows/test-master.yml index 9a07e435..53f7c826 100644 --- a/.github/workflows/test-master.yml +++ b/.github/workflows/test-master.yml @@ -20,7 +20,7 @@ jobs: strategy: matrix: os: ['ubuntu-latest', 'windows-latest'] - python-version: [ '3.6', '3.7', '3.8' ] + python-version: [ '3.8', '3.9', '3.10' ] steps: - uses: actions/checkout@v2 @@ -29,7 +29,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | - python -m pip install --upgrade pip==20.2.2 + python -m pip install --upgrade pip==23.1.2 python -m pip install -e . python nyoka/tests/_install_dependencies.py - name: Test with pytest @@ -42,10 +42,10 @@ jobs: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: - python-version: 3.6 + python-version: 3.8 - name: Install dependencies run: | - python -m pip install --upgrade pip==20.2.2 + python -m pip install --upgrade pip==23.1.2 python -m pip install -e . python nyoka/tests/_install_dependencies.py - name: Generate test coverage report diff --git a/README.md b/README.md index f6d96688..1592e4ce 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ Read the documentation at **[Nyoka Documentation](https://softwareag.github.io/n ## List of libraries and models supported by Nyoka : -### Scikit-Learn (version <= 0.23.1): +### Scikit-Learn: #### Models - @@ -74,14 +74,14 @@ Read the documentation at **[Nyoka Documentation](https://softwareag.github.io/n * [`LGBMRegressor`](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) -### XGBoost (version <= 1.5.2): +### XGBoost: * [`XGBClassifier`](https://xgboost.readthedocs.io/en/release_1.5.0/python/python_api.html#module-xgboost.sklearn) * [`XGBRegressor`](https://xgboost.readthedocs.io/en/release_1.5.0/python/python_api.html#module-xgboost.sklearn) -### Statsmodels (version <= 0.11.1): +### Statsmodels: * [`tsa.arima_model.ARIMA`](https://github.com/statsmodels/statsmodels/blob/master/statsmodels/tsa/arima_model.py#L1026) diff --git a/docs/.buildinfo b/docs/.buildinfo index c8be6f05..9336abac 100644 --- a/docs/.buildinfo +++ b/docs/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: cd9cd651081c4d496ec0a729bf364d67 +config: 03accd84b224698acf6acb00dd9a2f6f tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/.doctrees/PMML44.doctree b/docs/.doctrees/PMML44.doctree deleted file mode 100644 index 01b23b08..00000000 Binary files a/docs/.doctrees/PMML44.doctree and /dev/null differ diff --git a/docs/.doctrees/PMML44Super.doctree b/docs/.doctrees/PMML44Super.doctree deleted file mode 100644 index 81c0816b..00000000 Binary files a/docs/.doctrees/PMML44Super.doctree and /dev/null differ diff --git a/docs/.doctrees/arima.doctree b/docs/.doctrees/arima.doctree deleted file mode 100644 index 2da20524..00000000 Binary files a/docs/.doctrees/arima.doctree and /dev/null differ diff --git a/docs/.doctrees/enums.doctree b/docs/.doctrees/enums.doctree deleted file mode 100644 index f34b4b3e..00000000 Binary files a/docs/.doctrees/enums.doctree and /dev/null differ diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle index 93211617..f0274302 100644 Binary files a/docs/.doctrees/environment.pickle and b/docs/.doctrees/environment.pickle differ diff --git a/docs/.doctrees/exponential_smoothing.doctree b/docs/.doctrees/exponential_smoothing.doctree index 04fc3ef9..47798143 100644 Binary files a/docs/.doctrees/exponential_smoothing.doctree and b/docs/.doctrees/exponential_smoothing.doctree differ diff --git a/docs/.doctrees/index.doctree b/docs/.doctrees/index.doctree index 887e352c..88918f66 100644 Binary files a/docs/.doctrees/index.doctree and b/docs/.doctrees/index.doctree differ diff --git a/docs/.doctrees/keras_model_to_pmml.doctree b/docs/.doctrees/keras_model_to_pmml.doctree deleted file mode 100644 index bf581db9..00000000 Binary files a/docs/.doctrees/keras_model_to_pmml.doctree and /dev/null differ diff --git a/docs/.doctrees/lag.doctree b/docs/.doctrees/lag.doctree deleted file mode 100644 index 2e94809e..00000000 Binary files a/docs/.doctrees/lag.doctree and /dev/null differ diff --git a/docs/.doctrees/lgb_to_pmml.doctree b/docs/.doctrees/lgb_to_pmml.doctree index a9c4b7be..0a132ee4 100644 Binary files a/docs/.doctrees/lgb_to_pmml.doctree and b/docs/.doctrees/lgb_to_pmml.doctree differ diff --git a/docs/.doctrees/metadata.doctree b/docs/.doctrees/metadata.doctree deleted file mode 100644 index 73871faa..00000000 Binary files a/docs/.doctrees/metadata.doctree and /dev/null differ diff --git a/docs/.doctrees/pre_process.doctree b/docs/.doctrees/pre_process.doctree index 098a9dae..70716076 100644 Binary files a/docs/.doctrees/pre_process.doctree and b/docs/.doctrees/pre_process.doctree differ diff --git a/docs/.doctrees/retinanet.doctree b/docs/.doctrees/retinanet.doctree deleted file mode 100644 index b868c33d..00000000 Binary files a/docs/.doctrees/retinanet.doctree and /dev/null differ diff --git a/docs/.doctrees/skl_to_pmml.doctree b/docs/.doctrees/skl_to_pmml.doctree index abb4f0d0..5ff04d42 100644 Binary files a/docs/.doctrees/skl_to_pmml.doctree and b/docs/.doctrees/skl_to_pmml.doctree differ diff --git a/docs/.doctrees/statsmodels_to_pmml.doctree b/docs/.doctrees/statsmodels_to_pmml.doctree index 6ca0bb78..5b8b39ff 100644 Binary files a/docs/.doctrees/statsmodels_to_pmml.doctree and b/docs/.doctrees/statsmodels_to_pmml.doctree differ diff --git a/docs/.doctrees/xgboost_to_pmml.doctree b/docs/.doctrees/xgboost_to_pmml.doctree index b848750b..4af6ba89 100644 Binary files a/docs/.doctrees/xgboost_to_pmml.doctree and b/docs/.doctrees/xgboost_to_pmml.doctree differ diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/_modules/enums.html b/docs/_modules/enums.html deleted file mode 100644 index bf975393..00000000 --- a/docs/_modules/enums.html +++ /dev/null @@ -1,531 +0,0 @@ - - - - - - - - - - - Enums Module — Nyoka 4.2.0 Documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for Enums Module

-from enum import Enum
-
-
class MISSING_VALUE_TREATMENT_METHOD(Enum):
-    AS_IS = "asIs"
-    AS_MEAN = "asMean"
-    AS_MODE = "asMode"
-    AS_MEDIAN = "asMedian"
-    AS_VALUE = "asValue"
-    RETURN_INVALID = "returnInvalid"

-
-
class PMML_SCHEMA(Enum):
-    VERSION = "4.4"

-
-
class TREE_SPLIT_CHARACTERISTIC(Enum):
-    BINARY = "binarySplit"
-    MULTI = "multiSplit"

-
-
class FUNCTION(Enum):
-    ADDITION = "+"
-    SUBSTRACTTION = "-"
-    MULTIPLICATION = "*"
-    DIVISION = "/"
-    MIN = "min"
-    MAX = "max"
-    SUM = "sum"
-    AVERAGE = "avg"
-    MEDIAN = "median"
-    PRODUCT = "product"
-    LOG10 = "log10"
-    LOGN = "ln"
-    SQUARE_ROOT = "sqrt"
-    ABSOLUTE = "abs"
-    EXPONENT = "exp"
-    POWER = "pow"
-    THRESHOLD = "threshold"
-    FLOOR = "floor"
-    CEILING = "ceil"
-    ROUND = "round"
-    MODULO = "modulo"
-    IS_MISSING = "isMissing"
-    IS_NOT_MISSING = "isNotMissing"
-    IS_VALID = "isValid"
-    IS_NOT_VALID = "isNotValid"
-    EQUAL = "equal"
-    NOT_EQUAL = "notEqual"
-    LESS_THAN = "lessThan"
-    LESS_OR_EQUAL = "lessOrEqual"
-    GREATER_THAN = "greaterThan"
-    GREATER_OR_EQUAL = "greaterOrEqual"
-    AND = "and"
-    OR = "or"
-    NOT = "not"
-    IS_IN = "isIn"
-    IS_NOT_IN = "isNotIn"
-    IF = "if"
-    UPPERCASE = "uppercase"
-    LOWERCASE = "lowercase"
-    SUBSTRING = "substring"
-    TRIM_BLANKS = "trimBlanks"
-    CONCAT = "concat"
-    REPLACE = "replace"
-    MATCHES = "matches"
-    FORMAT_NUMBER = "formatNumber"
-    FORMAT_DATETIME = "formatDatetime"
-    DATEDAYS_SINCE_YEAR = "dateDaysSinceYear"
-    DATESECONDS_SINCE_YEAR = "dateSecondsSinceYear"
-    DATESECONDS_SINCE_MIDNIGHT = "dateSecondsSinceMidnight"
-    NORMAL_CDF = "normalCDF"
-    NORMAL_PDF = "normalPDF"
-    STANDARD_NORMAL_CDF = "stdNormalCDF"
-    STANDARD_NORMAL_PDF = "stdNormalPDF"
-    ERF = "erf"
-    NORMAL_IDF = "normalIDF"
-    STANDARD_NORMAL_IDF = "stdNormalIDF"
-    EXPM1 = "expm1"
-    HYPOT = "hypot"
-    LOGN1P = "ln1p"
-    RINT = "rint"
-    SIN = "sin"
-    ASIN = "asin"
-    SINH = "sinh"
-    COS = "cos"
-    ACOS = "acos"
-    COSH = "cosh"
-    TAN = "tan"
-    ATAN = "atan"
-    TANH = "tanh"

-
-
-
class CATEGORICAL_SCORING_METHOD(Enum):
-    MAJORITY_VOTE = "majorityVote"
-    WEIGHTED_MAJORITY_VOTE = "weightedMajorityVote"

-
-
class CONTINUOUS_SCORING_METHOD(Enum):
-    MEDIAN = "median"
-    AVERAGE = "average"
-    WEIGHTED_AVERAGE = "weightedAverage"

-
-
class REGRESSION_NORMALIZATION_METHOD(Enum):
-    SIMPLEMAX = "simplemax"
-    SOFTMAX = "softmax"
-    LOGISTIC = "logit"
-    PROBIT = "probit"
-    CLOGLOG = "cloglog"
-    EXPONENTIAL = "exp"
-    LOGLOG = "loglog"
-    CAUCHIT = "cauchit"

-
-
class ARRAY_TYPE(Enum):
-    INTEGER = "int"
-    REAL = "real"
-    STRING = "string"

-
-
class MINING_FUNCTION(Enum):
-    ASSOCIATION_RULES = "associationRules"
-    SEQUENCES = "sequences"
-    CLASSIFICATION = "classification"
-    REGRESSION = "regression"
-    CLUSTERING = "clustering"
-    TIMESERIES = "timeSeries"
-    MIXED = "mixed"

-
-
class SVM_REPRESENTATION(Enum):
-    SUPPORT_VECTORS = "SupportVectors"
-    COEFFICIENTS = "Coefficients"

-
-
class SVM_CLASSIFICATION_METHOD(Enum):
-    OVR = "OneAgainstAll"
-    OVO = "OneAgainstOne"

-
-
class MULTIPLE_MODEL_METHOD(Enum):
-    MAJORITY_VOTE = "majorityVote"
-    WEIGHTED_MAJORITY_VOTE = "weightedMajorityVote"
-    AVERAGE = "average"
-    WEIGHTED_AVERAGE = "weightedAverage"
-    MEDIAN = "median"
-    WEIGHTED_MEDIAN = "weightedMedian"
-    MAX = "max"
-    SUM = "sum"
-    WEIGHTED_SUM = "weightedSum"
-    SELECT_FIRST = "selectFirst"
-    SELECT_ALL = "selectAll"
-    MODEL_CHAIN = "modelChain"

-
-
class COMPARISON_MEASURE_KIND(Enum):
-    DISTANCE = "distance"
-    SIMILARITY = "similarity"

-
-
class CLUSTERING_FILED_COMPARE_FUNCTION(Enum):
-    ABSOLUTE_DIFF = "absDiff"
-    GAUSSIAN_SIMILARITY = "gaussSim"
-    DELTA = "delta"
-    EQUAL = "equal"
-    TABLE = "table"

-
-
class CLUSTERING_MODEL_CLASS(Enum):
-    CENTER_BASED = "centerBased"
-    DISTRIBUTION_BASED = "distributionBased"

-
-
class NN_NORMALIZATION_METHOD(Enum):
-    SIMPLEMAX = "simplemax"
-    SOFTMAX = "softmax"

-
-
class NN_ACTIVATION_FUNCTION(Enum):
-    THRESHOLD = "threshold"
-    LOGISTIC = "logistic"
-    TANH = "tanh"
-    IDENTITY = "identity"
-    EXPONENTIAL = "exponential"
-    RECIPROCAL = "reciprocal"
-    SQUARE = "square"
-    GAUSS = "Gauss"
-    SINE = "sine"
-    COSINE = "cosine"
-    ELLIOTT = "Elliott"
-    ARCTAN = "arctan"
-    RECTIFIER = "rectifier"
-    RADIALBASIS = "radialBasis"

-
-
class MAXIMUM_LIKELIHOOD_STAT_METHOD(Enum):
-    KALMAN = "kalman"
-    THETA_RECURSION = "thetaRecursion"

-
-
class ARIMA_PREDICTION_METHOD(Enum):
-    CSS = "conditionalLeastSquares"
-    MLE = "exactLeastSquares"

-
-
class EXPONENTIAL_SMOOTHING_SEASONALITY(Enum):
-    ADDITIVE = "additive"
-    MULTIPLICATIVE = "multiplicative"

-
-
class EXPONENTIAL_SMOOTHING_TREND(Enum):
-    ADDITIVE = "additive"
-    DAMPED_ADDITIVE = "damped_additive"
-    MULTIPLICATIVE = "multiplicative"
-    DAMPED_MULTIPLICATIVE = "damped_multiplicative"
-    POLYNOMIAL_EXPONENTIAL = "polynomial_exponential"

-
-
class EXPONENTIAL_SMOOTHING_AND_ARIMA_TRANSFORMATION(Enum):
-    LOGARITHMIC = "logarithmic"
-    SQUARE_ROOT = "squareroot"

-
-
class TIME_ANCHOR(Enum):
-    DATETIMEMILLISECONDS_SINCE_0 = "dateTimeMillisecondsSince[0]"
-    DATETIMEMILLISECONDS_SINCE_1960 = "dateTimeMillisecondsSince[1960]"
-    DATETIMEMILLISECONDS_SINCE_1970 = "dateTimeMillisecondsSince[1970]"
-    DATETIMEMILLISECONDS_SINCE_1980 = "dateTimeMillisecondsSince[1980]"
-    DATETIMESECONDS_SINCE_0 = "dateTimeSecondsSince[0]"
-    DATETIMESECONDS_SINCE_1960 = "dateTimeSecondsSince[1960]"
-    DATETIMESECONDS_SINCE_1970 = "dateTimeSecondsSince[1970]"
-    DATETIMESECONDS_SINCE_1980 = "dateTimeSecondsSince[1980]"
-    DATEDAYS_SINCE_0 = "dateDaysSince[0]"
-    DATEDAYS_SINCE_1960 = "dateDaysSince[1960]"
-    DATEDAYS_SINCE_1970 = "dateDaysSince[1970]"
-    DATEDAYS_SINCE_1980 = "dateDaysSince[1980]"
-    DATEMONTHS_SINCE_0 = "dateMonthsSince[0]"
-    DATEMONTHS_SINCE_1960 = "dateMonthsSince[1960]"
-    DATEMONTHS_SINCE_1970 = "dateMonthsSince[1970]"
-    DATEMONTHS_SINCE_1980 = "dateMonthsSince[1980]"
-    DATEYEARS_SINCE_0 = "dateYearsSince[0]"

-
-
class TIMESERIES_USAGE(Enum):
-    ORIGINAL = "original"
-    LOGICAL = "logical"
-    PREDICTION = "prediction"

-
-
class TIMESERIES_ALGORITHM(Enum):
-    ARIMA = "ARIMA"
-    EXPONENTIAL_SMOOTHING = "ExponentialSmoothing"
-    SEASONAL_TREND_DECOMPOSE = "SeasonalTrendDecomposition"
-    SPECTRAL_ANALYSIS = "SpectralAnalysis"
-    STATE_SPACE_MODEL = "StateSpaceModel"
-    GARCH = "GARCH"

-
-
class LAG_AGGREGATION(Enum):
-    AVERAGE = "avg"
-    MIN = "min"
-    MAX = "max"
-    MEDIAN = "median"
-    PRODUCT = "product"
-    SUM = "sum"
-    STANDARD_DEVIATION = "stddev"

-
-
class ANOMALY_DETECTION_ALGORITHM(Enum):
-    ISOLATION_FOREST = "iforest"
-    ONE_CLASS_SVM = "ocsvm"
-    CLUSTER_MEAN_DISTANCE = "clusterMeanDist"
-    OTHER = "other"

-
-
class SIMPLE_PREDICATE_OPERATOR(Enum):
-    EQUAL = "equal"
-    NOT_EQUAL = "notEqual"
-    LESS_THAN = "lessThan"
-    LESS_OR_EQUAL = "lessOrEqual"
-    GREATER_THAN = "greaterThan"
-    GREATER_OR_EQUAL = "greaterOrEqual"
-    IS_MISSING = "isMissing"
-    IS_NOT_MISSING = "isNotMissing"

-
-
class FIELD_USAGE_TYPE(Enum):
-    ACTIVE = "active"
-    PREDICTED = "predicted"
-    TARGET = "target"
-    SUPPLEMENTARY = "supplementary"
-    GROUP = "group"
-    ORDER = "order"
-    FREQUENCY_WEIGHT = "frequencyWeight"
-    ANALYSIS_WEIGHT = "analysisWeight"

-
-
class RESULT_FEATURE(Enum):
-    PREDICTED_VALUE = "predictedValue"
-    PREDCITED_DISPLAY_VALUE = "predictedDisplayValue"
-    TRANSFORMED_VALUE = "transformedValue"
-    DECISION = "decision"
-    PROBABILITY = "probability"
-    TOP_CATEGORIES = "topCategories"
-    AFFINITY = "affinity"
-    RESIDUAL = "residual"
-    STANDARD_ERROR = "standardError"
-    STANDARD_DEVIATION = "standardDeviation"
-    CLUSTER_ID = "clusterId"
-    CLUSTER_AFFINITY = "clusterAffinity"
-    ENTITY_ID = "entityId"
-    ENTITY_AFFINITY = "entityAffinity"
-    WARNING = "warning"
-    RULE_VALUE = "ruleValue"
-    REASON_CODE = "reasonCode"
-    ANTECEDENT = "antecedent"
-    CONSEQUENT = "consequent"
-    RULE = "rule"
-    RULE_ID = "ruleId"
-    CONFIDENCE = "confidence"
-    SUPPORT = "support"
-    LIFT = "lift"
-    LEVERAGE = "leverage"
-    CONFIDENCE_INTERVAL_UPPER = "confidenceIntervalUpper"
-    CONFIDENCE_INTERVAL_LOWER = "confidenceIntervalLower"

-
-
class OPTYPE(Enum):
-    CATEGORICAL = "categorical"
-    ORDINAL = "ordinal"
-    CONTINUOUS = "continuous"

-
-
class DATATYPE(Enum):
-    STRING = "string"
-    INTEGER = "integer"
-    BINARY = "binary"
-    FLOAT = "float"
-    DOUBLE = "double"
-    BOOLEAN = "boolean"
-    DATE = "date"
-    TIME = "time"
-    DATETIME = "dateTime"
-    DATEDAYS_SINCE_0 = "dateDaysSince[0]"
-    DATEDAYS_SINCE_1960 = "dateDaysSince[1960]"
-    DATEDAYS_SINCE_1970 = "dateDaysSince[1970]"
-    DATEDAYS_SINCE_1980 = "dateDaysSince[1980]"
-    TIMESECONDS = "timeSeconds"
-    DATETIMESECONDS_SINCE_0 = "dateTimeSecondsSince[0]"
-    DATETIMESECONDS_SINCE_1960 = "dateTimeSecondsSince[1960]"
-    DATETIMESECONDS_SINCE_1970 = "dateTimeSecondsSince[1970]"
-    DATETIMESECONDS_SINCE_1980 = "dateTimeSecondsSince[1980]"

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/exponential_smoothing.html b/docs/_modules/exponential_smoothing.html index 5c86496b..73f3ac57 100644 --- a/docs/_modules/exponential_smoothing.html +++ b/docs/_modules/exponential_smoothing.html @@ -1,185 +1,93 @@ - - - - + - - - - - ExponentialSmoothing Module — Nyoka 4.2.0 documentation - - - - - + + + exponential_smoothing — Nyoka 5.5.0 documentation + + + - - - - - - - - - - - - - - - - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/index.html b/docs/_modules/index.html index 7edd840e..6dc2c402 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -1,220 +1,110 @@ - - - - + - - - - - Overview: Module code — Nyoka 4.2.0 documentation - - - - - + + + Overview: module code — Nyoka 5.5.0 documentation + + + - - - - - - - - - - - - - - - - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/keras_model_to_pmml.html b/docs/_modules/keras_model_to_pmml.html deleted file mode 100644 index f0fdc704..00000000 --- a/docs/_modules/keras_model_to_pmml.html +++ /dev/null @@ -1,1003 +0,0 @@ - - - - - - - - - - - Keras Exporter — Nyoka 4.2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for Keras Exporter

-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-"""
-
-"""
-
-from __future__ import absolute_import
-
-import sys, os
-BASE_DIR = os.path.dirname(os.path.dirname(__file__))
-sys.path.append(BASE_DIR)
-
-import datetime
-import json
-
-import PMML44 as ny
-import metadata
-import warnings
-import base64
-
-warnings.formatwarning = warnings.formatwarning = lambda msg, *args, **kwargs: str(msg)+'\n'
-
-KERAS_LAYER_TYPES_MAP = {'InputLayer': 'Input',
-                         'Add': 'MergeLayer',
-                         'Concatenate': 'MergeLayer',
-                         'Dot': 'MergeLayer',
-                         'Subtract': 'MergeLayer',
-                         'Maximum': 'MergeLayer',
-                         'Minimum': 'MergeLayer',
-                         'Average': 'MergeLayer'}
-
-KERAS_LAYER_PARAMS = ['filters', 'kernel_size', 'strides', 'padding',
-                      'input_shape', 'output_shape', "activation", "axis",
-                      "epsilon", "pool_size", "scale", "center", "depth_multiplier",
-                      "rate", "dilation_rate","size","stride","ratios","scales","mean","std","nms_threshold",
-                      "score_threshold"]
-
-NYOKA_LAYER_PARAMS = ['featureMaps', 'kernel', 'stride', 'pad',
-                      'inputDimension', 'outputDimension',
-                      "activationFunction", "axis",
-                      "batchNormalizationEpsilon", "poolSize",
-                      "batchNormalizationScale", "batchNormalizationCenter", "depthMultiplier",
-                      "dropoutRate", "dilationRate","upsamplingSize","anchorStride","anchorRatios","anchorScales",
-                      "regressBoxesMean", "regressBoxesStd","nmsThreshold","scoreThreshold"]
-
-
-
class KerasHeader(ny.Header):
-    """
-    Creates header for Keras PMML model file using Nyoka
-
-    Parameters
-    ----------
-    description : String
-        Description of the PMML file provided as a default
-    copyright : String
-        Adds the information about the copyright.
-
-    Returns
-    -------
-    Nyoka header object
-    """ 
- 
-    def __init__(self, description, copyright):
-        if not description:
-            description = "Keras Model in PMML"
-        if not copyright:
-            copyright = "Copyright (c) 2018 Software AG"
-        ny.Header.__init__(self, copyright=copyright,
-                           description=description,
-                           Timestamp=ny.Timestamp(str(datetime.datetime.now())),
-                           Application=ny.Application(name="Nyoka",version=metadata.__version__))

-
-
-
class KerasNetworkLayer(ny.NetworkLayer):
-    """
-    Creates Networklayer of PMML which has information about the layer type, weight matrix and bias matrix and their properties.
-
-    Parameters
-    ----------
-    layer : Keras layer object
-        Keras layer object
-    dataSet : String
-        Name of the dataset
-    layer_type : String
-        Class name of the layer
-    script_args : Dictionary or None
-        Parameters for the script if any preprocessing script is provided
-    connection_layer_id : boolean
-        Whether to generate connection layer IDs or not
-
-    Returns
-    -------
-    Nyoka NetworkLayer object
-    """ 
-
-    def _get_flatten_weights(self, weights):
-        import numpy as np
-
-        """
-        Flattens the input
-
-        Parameters
-        ----------
-        weights : array
-            Array of weights
-
-        Returns
-        -------
-        flat_weights : array
-            Flattened input array in Base64String format
-
-        weights_shape : array
-            Shape of the flattened array
-        """ 
-        flat_weights = []
-        weights_shape = []
-        if weights and isinstance(weights, list):
-            for item in weights:
-                weights_shape.append(str(item.shape))
-                flat_weights.append(item.flatten())
-            flat_weights = np.concatenate(flat_weights).ravel()
-            if len(weights) > 1:
-                weights_shape = str((len(weights_shape), item.shape[0], 1))
-            else:
-                weights_shape = ", ".join(weights_shape)
-        return flat_weights, weights_shape
-
-    def _get_enumerated_input_shape(self, input_shape):
-        """
-        Gets the input shape from the Keras Input layer 
-
-        Parameters
-        ----------
-        input_shape : array
-            Array of shape
-
-        Returns
-        -------
-        input_dims : array
-            Array of shape for Nyoka PMML
-        """ 
-        input_dims = None
-        if isinstance(input_shape, tuple):
-            in_s = input_shape[1:]
-            if len(in_s) == 1:
-                input_dims = str((in_s[0], 1))
-            else:
-                input_dims = str(in_s)
-        elif isinstance(input_shape, list):
-            new_shape_lst = []
-            for i_shape in input_shape:
-                new_shape_lst.append(str(tuple(i_shape[1:])))
-            input_dims = ", ".join(new_shape_lst)
-        return input_dims
-
-    def _get_activation_function(self, layer):
-        """
-        Identifies the Activation Function from a given Keras layer
-
-        Parameters
-        ----------
-        layer : Keras layer object
-            A Keras Layer
-
-        Returns
-        -------
-        activation_function : String
-            Activation function of the given Keras layer
-
-        """ 
-        layer_config = layer.get_config()
-        if 'activation' in layer_config:
-            activation_function = layer_config.get('activation')
-            if activation_function == "relu":
-                activation_function = "rectifier"
-            elif activation_function == "relu6":
-                activation_function = "reLU6"
-            elif activation_function == "tanh":
-                activation_function = "tanch"
-        else:
-            activation_function = None
-        return activation_function
-
-    def _get_layer_params_dict(self, layer):
-        """
-        Pull out the relevant Nyoka layer attributes matching with Keras layer attributes and pulls values for the respective attributes
-
-        Parameters
-        ----------
-        layer : Keras layer object
-            A Keras Layer
-
-        Returns
-        -------
-        new_layer_params_dict : Dictionary
-            Nyoka Layer attributes in a dictionary format
-
-        """ 
-        layer_params_dict = dict(zip(NYOKA_LAYER_PARAMS, KERAS_LAYER_PARAMS))
-        layer_config = layer.get_config()
-        new_layer_params_dict = {}
-        pad_dims = None
-        layer_params_dict['paddingDims']='None'  
-        for key, val in layer_params_dict.items():
-            if val in layer_config:
-                if val == "activation":
-                    layer_params_dict[key] = self._get_activation_function(layer)
-                elif val == "padding":
-                    pad_val = layer_config.get(val)
-                    if pad_val in ['valid','same']:
-                        layer_params_dict[key] = str(pad_val)
-                    else:
-                        pad_dims=str(pad_val)
-                        layer_params_dict['paddingDims'] = pad_dims
-                else:
-                    layer_params_dict[key] = str(tuple(layer_config.get(val)))\
-                         if layer_config.get(val).__class__.__name__ == 'list' else str(layer_config.get(val))
-            elif hasattr(layer, val):
-                if val == "input_shape":
-                    try:
-                        shape = getattr(layer, val)
-                    except:
-                        shape = layer.get_input_shape_at(0) 
-                    layer_params_dict[key] = self._get_enumerated_input_shape(shape)
-                elif val == "output_shape":
-                    try:
-                        shape = getattr(layer, val)
-                    except:
-                        shape = layer.get_output_shape_at(0) 
-                    layer_params_dict[key] = self._get_enumerated_input_shape(shape)
-                else:
-                    layer_params_dict[key] = str(tuple(getattr(layer,val)))\
-                         if getattr(layer,val).__class__.__name__ == 'list' else str(getattr(layer,val))
-            else:
-                layer_params_dict[key] = None
-            if layer_params_dict[key] and layer_params_dict[key] != "None":
-                if key == "pad" and pad_dims:
-                    pad_ = list()
-                    for val in pad_val:
-                        if hasattr(val,'__len__'):
-                            for v in val:
-                                pad_.append(v)
-                        else:
-                            pad_.append(val)
-                    pad_val = tuple(pad_)
-                    new_layer_params_dict["paddingDims"] = str(pad_val)
-                else:
-                    new_layer_params_dict[key] = layer_params_dict[key]
-        return new_layer_params_dict
-
-    def _get_layer_weights_n_biases(self, layer):
-        """
-        Pulls out the Weights and Bias matrix from a given Keras layer
-
-        Parameters
-        ----------
-        layer : Keras layer object
-            A Keras Layer
-
-        Returns
-        -------
-        layer_weights : array
-            Weights of the Keras layer in Base64String format
-        layer_biases : array
-            Bias of the Keras layer in Base64String format
-
-        """ 
-        layer_all_weights = layer.get_weights()
-        layer_weights = layer_biases = biases = None
-        if layer_all_weights:
-            if hasattr(layer, 'use_bias') and layer.use_bias:
-                biases = layer_all_weights[-1]
-                weights, w_shape = self._get_flatten_weights(
-                    layer_all_weights[0:-1])
-                layer_weights = ny.LayerWeights(content=weights,
-                                                floatsPerLine=0,
-                                                weightsShape=w_shape,
-                                                weightsFlattenAxis="0")
-            else:
-                weights, w_shape = self._get_flatten_weights(layer_all_weights)
-                layer_weights = ny.LayerWeights(content=weights,
-                                                floatsPerLine=0,
-                                                weightsShape=w_shape,
-                                                weightsFlattenAxis="0")
-
-            if biases is not None:
-                bs_shape = biases.shape
-                if len(bs_shape) == 1:
-                    final_bs_shape = str((bs_shape[0], 1))
-                else:
-                    final_bs_shape = str(bs_shape)
-                layer_biases = ny.LayerBias(content=biases,
-                                            biasShape=final_bs_shape,
-                                            biasFlattenAxis="0",
-                                            floatsPerLine=0)
-        return layer_weights, layer_biases
-
-    def _get_connection_layer_ids(self, layer):
-        """
-        Pulls out the Connection ID of the Keras layer
-
-        Parameters
-        ----------
-        layer : Keras layer object
-            A Keras Layer
-
-        Returns
-        -------
-        connection_layers : String
-            The Layer ID of the Keras Layer
-
-        """ 
-        node_config = layer._inbound_nodes[0].get_config()
-        if node_config['inbound_layers']:
-            inbound_layers = node_config['inbound_layers']
-            connection_layers = ", ".join(inbound_layers)
-        else:
-            connection_layers = "na"
-        return connection_layers
-
-    def __init__(self, layer,dataSet, layer_type, script_args, connection_layer_id=True):
-        merge_layer_op_type = None
-        merge_concat_axes = None
-        merge_dot_axes = None
-        merge_dot_normalization = False
-        connection_layers = ''
-        input_field_name = None
-        if "Pmml" in layer_type:
-            layer_type = layer_type[4:]
-        old_layer_type = layer_type
-        layer_type = KERAS_LAYER_TYPES_MAP.get(layer_type, layer_type)
-        layer_params = self._get_layer_params_dict(layer)
-        layer_weights, layer_biases = self._get_layer_weights_n_biases(layer)
-        if connection_layer_id:
-            connection_layers = self._get_connection_layer_ids(layer)
-        if layer_type == "MergeLayer":
-            merge_layer_op_type = old_layer_type.lower()
-            if merge_layer_op_type == "concatenate":
-                if "axis" in layer_params:
-                    merge_concat_axes = layer_params["axis"]
-                    del layer_params["axis"]
-            elif merge_layer_op_type == 'dot':
-                if 'axes' in layer_params:
-                    merge_dot_axes = layer_params["axes"]
-                    del layer_params["axes"]
-                if 'normalize' in layer_params:
-                    merge_dot_normalization = layer_params["normalize"]
-                    del layer_params["normalize"]
-                layer_params["mergeLayerDotNormalize"] = merge_dot_normalization
-        elif layer_type == "BatchNormalization":
-            layer_type = "BatchNormalization"
-            if "axis" in layer_params:
-                layer_params["batchNormalizationAxis"] = layer_params["axis"]
-                del layer_params["axis"]
-            if "batchNormalizationScale" in layer_params:
-                try:
-                    layer_params["batchNormalizationScale"] = eval(layer_params[
-                        "batchNormalizationScale"])
-                except:
-                    layer_params["batchNormalizationScale"] = layer_params[
-                        "batchNormalizationScale"]
-            if "batchNormalizationCenter" in layer_params:
-                try:
-                    layer_params["batchNormalizationCenter"] = eval(layer_params[
-                        "batchNormalizationCenter"])
-                except:
-                    layer_params["batchNormalizationCenter"] = layer_params[
-                        "batchNormalizationCenter"]
-        elif layer_type == "ReLU":
-            layer_type = "Activation"
-            layer_params["activationFunction"] = "reLU6"
-        elif layer_type == "Anchors":
-            layer_params["anchorSize"] = layer_params["upsamplingSize"]
-            del layer_params["upsamplingSize"]
-            layer_params["anchorRatios"] = layer_params["anchorRatios"]
-            layer_params["anchorScales"] = layer_params["anchorScales"]
-        elif layer_type == "RegressBoxes":
-            layer_params["regressBoxesMean"] = layer_params["regressBoxesMean"]
-            layer_params["regressBoxesStd"] = layer_params["regressBoxesStd"]
-        elif layer_type == 'FilterDetections':
-            layer_params['nms'] = layer.nms
-            layer_params['classSpecificFilter'] = layer.class_specific_filter
-        layer_params["mergeLayerOp"] = merge_layer_op_type
-        layer_params["mergeLayerConcatOperationAxes"] = merge_concat_axes
-        layer_params["mergeLayerDotOperationAxis"] = merge_dot_axes
-        if layer_type == "Input":
-            if dataSet=='image' or script_args:
-                input_field_name = 'base64String'
-            else:
-                input_field_name = dataSet
-        ny.NetworkLayer.__init__(self, inputFieldName=input_field_name,
-                                 layerType=layer_type,
-                                 connectionLayerId=connection_layers,
-                                 layerId=layer.name,
-                                 normalizationMethod="none",
-                                 LayerParameters=ny.LayerParameters(
-                                     **layer_params),
-                                 LayerWeights=layer_weights,
-                                 LayerBias=layer_biases)

-
-
-
class KerasDataDictionary(ny.DataDictionary):
-    """
-    KerasDataDictionary stores the class information to be predicted  in the PMML model.
-    The current implementation takes care of the image class label by giving dataset name as dataSet parameter.
-
-    Parameters
-    ----------
-    dataSet : String
-        Name of the dataset
-    predictedClasses : List
-        List of class names or values to be predicted.
-    script_args : Dictionary or None
-        Parameters for the script if any preprocessing script is provided
-    Returns
-    -------
-    Nyoka's DataDictionary Object
-    """ 
-    def __init__(self, dataSet, predictedClasses, script_args):
-        ny.DataDictionary.__init__(self)
-        if predictedClasses:
-            class_node = ny.DataField(name="labels", optype="categorical",
-                                    dataType="string")
-            if type(predictedClasses) == list:
-                if not all(type(pC) == str for pC in predictedClasses):
-                    warnings.warn(
-                        "Not all classes are given as String. Values will be attempted to be converted to String.")
-                for i in range(len(predictedClasses)):
-                    data_val = predictedClasses[i]
-                    class_node.add_Value(ny.Value(value=str(data_val)))
-            elif type(predictedClasses) == dict:
-                if not all(type(pC) == str for pC in predictedClasses.keys()):
-                    warnings.warn(
-                        "Class indices are expected as strings in dictionary keys. Keys will be attempted to be converted to String.")
-                for i in range(len(predictedClasses.keys())):
-                    data_val = predictedClasses.keys()[i]
-                    class_node.add_Value(ny.Value(value=str(data_val)))
-        else:
-            class_node = ny.DataField(name="predictions", optype="continuous",
-                                        dataType="double")
-        if dataSet == "image" or script_args:
-            name = dataSet
-            ny.DataDictionary.add_DataField(self, ny.DataField(
-                name=name, optype="categorical", dataType="binary",
-                mimeType="image/png", Extension=[ny.Extension(
-                    extender="ADAPA", name="BINARY_BUFFERED", value="true")]))
-        else:
-            name = dataSet
-            ny.DataDictionary.add_DataField(self, ny.DataField(
-                name=name, optype="categorical", dataType="string"
-            ))           
-
-        ny.DataDictionary.add_DataField(self, class_node)

-
-
-
class KerasMiningSchema(ny.MiningSchema):
-    """
-    KerasMiningSchema stores the attributes which are used to build the model.
-    
-    Parameters
-    ----------
-    dataSet : String
-        Name of the dataset
-    predictedClasses : List
-        List of class names or values to be predicted.
-
-    Returns
-    -------
-    Nyoka's MiningSchema Object
-    """ 
-    def __init__(self, dataSet, predictedClasses):
-        ny.MiningSchema.__init__(self)
-        name = dataSet
-        ny.MiningSchema.add_MiningField(self, ny.MiningField(
-            name=name, usageType="active",
-            invalidValueTreatment="asIs"))
-
-        ny.MiningSchema.add_MiningField(self, ny.MiningField(
-            name="labels" if predictedClasses else "predictions", usageType="target",
-            invalidValueTreatment="asIs"))

-
-
-
class KerasOutput(ny.Output):
-    """
-    KerasOutput provides the information about the output representation of the PMML. (e.g. Predicted classes, probabilities)
-    
-    Parameters
-    ----------
-    predictedClasses : List or None
-        List of Classes for which model has been trained. If not provided, considered as Regression
-
-    Returns
-    -------
-    Nyoka's Output Object
-    """ 
-    def __init__(self, predictedClasses=None):
-        ny.Output.__init__(self)
-        if predictedClasses:
-            ny.Output.add_OutputField(self, ny.OutputField(
-                name="predicted_label", feature="predictedValue",
-                dataType="string", optype="categorical"))
-            ny.Output.add_OutputField(self, ny.OutputField(
-                name="top1_prob", feature="probability", dataType="double"))
-            ny.Output.add_OutputField(self, ny.OutputField(
-                name="top5_prob", feature="topCategories", numTopCategories="5",
-                dataType="string", optype="categorical"))
-        else:
-            ny.Output.add_OutputField(self, ny.OutputField(
-                name="predicted_predictions", feature="predictedValue",
-                dataType="double", optype="continuous"))

-
-
-
-
class KerasLocalTransformations(ny.LocalTransformations):
-    """
-    KerasLocalTransformations provides the information about the list of transformations applied to the data.
-    
-    Parameters
-    ----------
-    keras_model : Keras model object
-        Keras model object
-    dataSet : String
-        Name of the dataset
-    script_args : Dictionary
-        Parameters for the script if any preprocessing script is provided
-
-    Returns
-    -------
-    Nyoka's LocalTransformations Object
-    """ 
-    def __init__(self, keras_model, dataSet, script_args):
-        ny.LocalTransformations.__init__(self)
-        if script_args:
-            ret_type = script_args['return_type'].lower()
-            def_name = script_args['def_name'] if script_args['content'].__class__.__name__=='str' else script_args['content'].__name__
-
-            ny.LocalTransformations.add_DerivedField(self, ny.DerivedField(
-                name="base64String", optype="categorical" if ret_type=="string" else "continuous",
-                dataType=ret_type, Apply=ny.Apply(function='customFunc',
-                FieldRef=[ny.FieldRef(field=dataSet)])
-            ))
-        else:
-            ny.LocalTransformations.add_DerivedField(self, ny.DerivedField(
-                name="base64String", optype="categorical", dataType="string",
-                Apply=ny.Apply(function="CNN:getBase64String",
-                            FieldRef=[ny.FieldRef(field="image")])))

-
-
-
class KerasTransformationDictionary(ny.TransformationDictionary):
-    """
-    KerasTransformationDictionary provides the information about the list of transformations functions applied to the data.
-    
-    Parameters
-    ----------
-    dataSet : string
-        name of the input
-    script_args : Dictionary
-        Parameters for the script if any preprocessing script is provided
-
-    Returns
-    -------
-    Nyoka's TransformationDictionary object 
-    """
-    def __init__(self, dataSet, script_args):
-        if script_args['content'].__class__.__name__ == 'str':
-            content = script_args['content']
-            def_name = script_args['def_name']
-        else:
-            import inspect
-            content = inspect.getsource(script_args['content'])
-            def_name = script_args['content'].__name__
-        encode = True
-        if "encode" in script_args:
-            encode = script_args['encode']
-        
-        if encode:
-            content = base64.b64encode(content.encode()).decode()
-        return_type = script_args['return_type'].lower()
-        extension = [ny.Extension(
-            extender='ADAPA', 
-            name=def_name, 
-            value=return_type,
-            anytypeobjs_=[content]
-            )]
-        def_func = ny.DefineFunction(
-            name='customFunc',
-            optype='categorical' if return_type == 'string' else 'continous',
-            dataType=return_type,
-            ParameterField=[ny.ParameterField(name=dataSet, dataType='binary')],
-            Apply = ny.Apply(function='python', Extension=extension, FieldRef=[ny.FieldRef(field=dataSet)]),
-
-        )
-        ny.TransformationDictionary.__init__(self)
-        ny.TransformationDictionary.add_DefineFunction(self, def_func)

-
-
-
-
class KerasNetwork(ny.DeepNetwork):
-    """
-    KerasNetwork creates the DeepNetwork object which stores the NetworkLayer in sequence to define the architecture.
-    
-    Parameters
-    ----------
-    keras_model : Keras model object
-        Keras model object
-    model_name : String
-        Name of the model
-    dataSet : String
-        Name of the dataset
-    predictedClasses : List or None
-        List of class names
-    script_args : Dictionary or None
-        Parameters for the script if any preprocessing script is provided
-
-    Returns
-    -------
-    Nyoka's DeepNetwork Object
-    """ 
-
-
-    def _create_an_input_layer(self, layer, dataSet, script_args):
-        """
-        Creates a PMML input layer from Keras Input Layer object
-        
-        Parameters
-        ----------
-        layer : Keras layer object
-            Keras layer object
-        dataSet : String
-            Name of the dataset
-    
-        Returns
-        -------
-        input_layer: Nyoka Object
-            PMML Input layer object
-        """
-
-        
-        if dataSet=='image' or script_args:
-            inputField = "base64String"
-        else:
-            inputField = dataSet
-
-        in_shape = layer.input_shape
-        if in_shape[0] is not None:
-            if len(in_shape) == 1:
-                input_dims = output_dims = str((in_shape[0],1))
-            else:
-                input_dims = output_dims = str(in_shape)  
-        else:
-            if len(in_shape) == 2:
-                input_dims = output_dims = str(tuple(list(in_shape[1:])+[1]))
-            else:
-                input_dims = output_dims = str(tuple(list(in_shape[1:])))  
-        node_config = layer._inbound_nodes[0].get_config()
-        connection_layers = ", ".join(node_config['inbound_layers'])
-        input_layer = ny.NetworkLayer(
-            inputFieldName=inputField, layerType="Input", layerId=connection_layers,
-            connectionLayerId="na",LayerParameters=ny.LayerParameters(
-                inputDimension=input_dims,
-                outputDimension=output_dims))
-        return input_layer
-
-    def _create_layers(self, keras_model, dataSet, script_args):
-        """
-        Create list of PMML network layers from Keras Model object.
-        
-        Parameters
-        ----------
-        keras_model : Keras model object
-            Keras model object
-        dataSet : String
-            Name of the dataset
-    
-        Returns
-        -------
-        network_layers: Nyoka Object
-            PMML network layer object 
-        """
-        network_layers = []
-        model_layers = keras_model.layers
-        first_layer = model_layers[0]
-        if first_layer.__class__.__name__ != "InputLayer":
-            input_layer = self._create_an_input_layer(first_layer, dataSet, script_args)
-            if input_layer:
-                network_layers.append(input_layer)
-        for layer in model_layers:
-            layer_type = layer.__class__.__name__
-            net_layer = KerasNetworkLayer(layer,dataSet, layer_type,script_args)
-            network_layers.append(net_layer)
-        return network_layers
-
-    def __init__(self, keras_model, model_name, dataSet=None, predictedClasses=None, script_args=None):
-        if not model_name:
-            model_namme = keras_model.name
-        network_layers = self._create_layers(keras_model, dataSet, script_args)
-        local_trans = None
-        mining_schema = KerasMiningSchema(dataSet, predictedClasses)
-        if dataSet == 'image' or script_args:
-            local_trans = KerasLocalTransformations(keras_model, dataSet, script_args)
-        function_Name = "classification" if predictedClasses else "regression"
-        ny.DeepNetwork.__init__(self, modelName=model_name,
-                                functionName=function_Name, algorithmName=None,
-                                normalizationMethod="none", numberOfLayers=len(network_layers),
-                                isScorable=True, Extension=None, MiningSchema=mining_schema,
-                                Output=KerasOutput(predictedClasses), LocalTransformations=local_trans,
-                                ModelStats=None, ModelExplanation=None, Targets=None,
-                                NetworkLayer=network_layers, NeuralOutputs=None,
-                                ModelVerification=None)

-
-
-
class KerasToPmml(ny.PMML):
-    """
-    KerasToPmml exports the Keras model object into PMML file using nyoka.
-    
-    Parameters
-    ----------
-    keras_model : Keras model object
-        Keras model object 
-    model_name : String or None
-        Name to be given to the model in PMML.
-    description : Sting or None
-        Description to be shown in PMML
-    dataSet : String or None
-        Name of the dataset. Value is 'image' for Image Classifier, 'None' or any other value is for tabular or base64 encoded data. 
-    predictedClasses : List or None
-        List of the class names for which model has been trained. If not provided, assumed to be regression model.
-    script_args : Dictionary or None
-        Contains information of the script to be used to convert `image` data into base64 string. Required when dataSet=`image`.
-        Required attributes - 
-            content : string or function
-                The content of the script
-            def_name : string
-                name of the function to be used. Required when content is string
-            return_type : string
-                The return type of the function. Valid values are ('string', 'double', 'float','integer')
-            encode : boolean
-                The representation of the script in PMML. If True, the script will be represented as base64 encoded string, else as plain text.
-                If not provided, default value `True` is considered.
-
-    Returns
-    -------
-    Creates Nyoka's PMML object, this can be saved in file using export function
-    """
-
-    @property
-    def content_error(self):
-        return "`content` should be present in script_args, which is either a function or a string (script content)"
-
-    @property
-    def def_name_error(self):
-        return "`def_name`, the name of the funciton is required when `content` is a string."
-
-    @property
-    def ret_type_error(self):
-        return "`return_type` of the preprocessing function is required. Valid return types are ('string', 'double', 'float', 'intger')"
-
-    @property
-    def ret_type_value_error(self):
-        return "Valid return types are ('string', 'double', 'float', 'intger')"
-
-    @property
-    def encode_error(self):
-        return "Valid values for `encode` are (True, False)"
-    
-
    def validate_script_args(self, script_args):
-        assert 'content' in script_args, self.content_error
-        if script_args['content'].__class__.__name__ == 'str':
-            assert 'def_name' in script_args, self.def_name_error
-        assert 'return_type' in script_args, self.ret_type_error
-        ret_type = script_args['return_type'].lower()
-        assert ret_type in ("string", "double", "float", "intger"), self.ret_type_value_error
-        if 'encode' in script_args:
-            assert script_args['encode'] in [True, False], self.encode_error

-
-
-    def __init__(self, keras_model, model_name=None, description=None,copyright=None,\
-        dataSet=None, predictedClasses=None, script_args=None):
-        if not dataSet:
-            dataSet = 'input'
-        data_dict = KerasDataDictionary(dataSet, predictedClasses, script_args)
-        trans_dict = None
-        if script_args:
-            self.validate_script_args(script_args)
-            trans_dict = KerasTransformationDictionary(dataSet,script_args)
-        super(KerasToPmml, self).__init__(
-            version="4.4", Header=KerasHeader(description=description, copyright=copyright),
-            DataDictionary=data_dict, TransformationDictionary= trans_dict, DeepNetwork=[
-                KerasNetwork(keras_model=keras_model, 
-                model_name=model_name, 
-                dataSet=dataSet, 
-                predictedClasses=predictedClasses,
-                script_args=script_args)])

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/lag.html b/docs/_modules/lag.html deleted file mode 100644 index 43f28ef8..00000000 --- a/docs/_modules/lag.html +++ /dev/null @@ -1,292 +0,0 @@ - - - - - - - - - - - Nyoka's Pre-Processing Module — Nyoka 4.2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for Nyoka's Pre-Processing Module

-import queue
-import numpy as np
-from sklearn.utils import check_array
-from sklearn.base import TransformerMixin
-FLOAT_DTYPES = (np.float64, np.float32, np.float16)
-
-
-
class Lag(TransformerMixin):
-    """
-    The Lag class takes `value` number of previous record of the fields where it is applied and applies `aggregation` to those values.
-
-    Parameters
-    ----------
-    aggregation : String
-        aggregation type. The valid types are ["min", "max", "sum", "avg", "median", "product", "stddev"]
-    value : Integer (default = 1)
-        The number of previous record to aggregate
-
-    
-    """
-    
-    _VALID_AGGS = ["min", "max", "sum", "avg", "median", "product", "stddev"]
-    _AGG_FUNC_MAP = {
-        "min" : np.min,
-        "max" : np.max,
-        "sum" : np.sum,
-        "avg" : np.mean,
-        "median" : np.median,
-        "product" : np.product,
-        "stddev" : np.std
-    }
-    
-    def __init__(self, aggregation, value=1, copy=True):
-        assert aggregation in self._VALID_AGGS, f"Invalid `aggregation` type. Valid types are {self._VALID_AGGS}"
-        self.aggregation = aggregation
-        self.value = value
-        self.copy = copy
-        
-
    def fit(self, X, y=None):
-        """
-        Does nothing.
-
-        Returns
-        -------
-        The same object
-        """   
-        return self

-            
-        
-
    def transform(self, X, y=None):
-        """
-        Trasforms the given X by taking `value` number of previous records and applying `aggregation` method
-
-        Parameters
-        ----------
-        X : Pandas DataFrame or numpy array
-            The input data
-        y : 
-            It is ignored.
-
-        Returns
-        -------
-        Transformed X as numpy array  
-        """
-        self._transformed_X = list()
-        X = check_array(X, copy=self.copy, warn_on_dtype=True, estimator=self)       
-        q_list = [queue.Queue() for i in range(len(X[0]))]
-            
-        for _ in range(self.value):
-            for q_ in q_list:
-                q_.put(0.0)
-        
-        for row in X:
-            aggregated_vals = [self._AGG_FUNC_MAP[self.aggregation](q_.queue) for q_ in q_list]
-            self._transformed_X.append(aggregated_vals)
-            for idx, col in enumerate(row):
-                q_list[idx].put(col)
-                q_list[idx].get()
-        return np.array(self._transformed_X)

-        
-    
-    def __repr__(self):
-        return f"Lag(aggregation='{self.aggregation}', value={self.value})"

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/lgb_to_pmml.html b/docs/_modules/lgb_to_pmml.html index 23a08906..b2fa6175 100644 --- a/docs/_modules/lgb_to_pmml.html +++ b/docs/_modules/lgb_to_pmml.html @@ -1,186 +1,94 @@ - - - - + - - - - - LightGBM Exporter — Nyoka 4.2.0 documentation - - - - - + + + lgb_to_pmml — Nyoka 5.5.0 documentation + + + - - - - - - - - - - - - - - - - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/pre_process.html b/docs/_modules/pre_process.html index 2aaec498..ff870fc5 100644 --- a/docs/_modules/pre_process.html +++ b/docs/_modules/pre_process.html @@ -1,181 +1,89 @@ - - - - + - - - - - Pre-Processing Exporter — Nyoka 4.2.0 documentation - - - - - + + + pre_process — Nyoka 5.5.0 documentation + + + - - - - - - - - - - - - - - - - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/retinanet.html b/docs/_modules/retinanet.html deleted file mode 100644 index 1676ddcf..00000000 --- a/docs/_modules/retinanet.html +++ /dev/null @@ -1,584 +0,0 @@ - - - - - - - - - - - RetinaNet Exporter — Nyoka 4.2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for RetinaNet Exporter

-from __future__ import absolute_import
-
-import sys
-import os
-import warnings
-BASE_DIR = os.path.dirname(os.path.dirname(__file__))
-sys.path.append(BASE_DIR)
-warnings.formatwarning = lambda msg, *args, **kwargs: str(msg)+'\n'
-
-import PMML44 as pml
-
-import keras_model_to_pmml as kerasAPI
-
-
class RetinanetToPmml:
-    
-    """
-    Write a PMML file for RetinaNet model.
-
-    Parameters
-    -----------
-    model : 
-        RetinaNet model object
-    input_shape : tuple 
-        Expected shape of the images to be scored
-    backbone_name : string
-        Name of backbone used to build the model. Valid values are `['resnet', 'mobilenet', 'densenet', 'vgg']`
-    input_format : string (optional. default='image')
-        Input format to be used during inference with the PMML. Valid values are - 
-            "image" : Original image in png format
-            "encoded" : Base64 encoded string of the image
-    trained_classes : list or tuple
-        List of the classes on which the model was trained. If not provided, default(1 to 80) classes will be used
-    pmml_file_name : string (default='from_retinanet.pmml')
-        Name of the PMML file
-    script_args : Dictionary or None
-        Contains information of the script to be used to convert `image` data into base64 string. Required when dataSet=`image`.
-        Required attributes - 
-            content : string or function
-                The content of the script
-            def_name : string
-                name of the function to be used. Required when content is string
-            return_type : string
-                The return type of the function. Valid values are ('string', 'double', 'float','integer')
-            encode : boolean
-                The representation of the script in PMML. If True, the script will be represented as base64 encoded string, else as plain text.
-                If not provided, default value `True` is considered.
-    model_name : string (optional)
-        Name of the model
-    description : string (optional)
-        Description of the model
-
-    Returns
-    -------
-    Creates Nyoka's PMML object and exports it to `pmml_file_name`
-    
-    """
-
-
    def inference_error(self):
-        return "Given model is not an inference model!"

-
-
    def input_format_error(self):
-        return "Invalid input_format type. Valid values are `['image', 'encoded']`"

-
-
    def backbone_name_error(self):
-        return "Invalid backbone_name. Valid values are `['resnet', 'mobilenet', 'densenet', 'vgg']`"

-
-    def __init__(self, model, input_shape, backbone_name, input_format="image", trained_classes=None,
-     pmml_file_name="from_retinanet.pmml", script_args=None, model_name=None, description=None):
-        assert model.layers[-1].__class__.__name__ == 'FilterDetections', self.inference_error()
-        assert input_format in ['image','encoded'], self.input_format_error()
-        assert backbone_name in ['resnet', 'mobilenet', 'densenet', 'vgg'], self.backbone_name_error()
-
-        self.backbone_name = backbone_name
-        self.model = model
-        self.input_shape = input_shape
-        self.input_format = input_format
-        self.script_args = script_args
-        self.model_name = model_name if model_name else "KerasRetinaNet"+input_format.title()
-        self.description = description if description else "RetinaNet model in PMML"
-
-        self.pmml_obj = None
-        self._pyramid_layers = ("P3", "P4", "P5", "P6", "P7")
-        self._layer_outputs = dict()
-
-        self.generate_pmml(model, input_shape,input_format,trained_classes)
-        self.pmml_obj.export(open(pmml_file_name,'w'),0)
-
-
-
    def generate_beckbone_anchors(self, model, input_format, trained_classes):
-        """
-        Generates PMML object for the backbone + anchors
-
-        Parameters
-        ----------
-        model : 
-            RetinaNet model object
-        input_format : string
-            Input format to be used during inference with the PMML. Valid values are - 
-                "image" : Original image in png format
-                "encoded" : Base64 encoded string of the image
-        trained_classes : List
-            List of class names for which the model was trained
-
-        Returns
-        -------
-        Nyoka's PMML object
-
-        """
-        from keras.models import Sequential
-        mod = Sequential()
-        for l in model.layers[1:]:
-            if l.__class__.__name__ == "Model":
-                break
-            mod.add(l)
-        if trained_classes == None:
-            warnings.warn(f"trained_classes are not provided. Maximum 80 classes will be considered.")
-            trained_classes = ["Category_"+str(i+1).zfill(2) for i in range(80)]
-
-        group1_pmml = kerasAPI.KerasToPmml(mod,model_name=self.model_name,dataSet=input_format, description=self.description,
-         predictedClasses=trained_classes, script_args=self.script_args)
-        return group1_pmml

-
-    
-
    def generate_submodel(self, submodel):
-        """
-        Generates multiple PMML object for the regression and classification submodel of RetinaNet for each connected pyramid layers
-
-        Parameters
-        ----------
-        submodel :
-            The Regression or the Classification submodel
-
-        Returns
-        -------
-        List of Nyoka's NetworkLayer object for all the submodels
-        """
-        net_layers_group=list()
-        for idx, name in enumerate(self._pyramid_layers):
-            nyoka_pmml_reg_mod = kerasAPI.KerasToPmml(submodel)
-            del nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer[0]
-            nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer[0].connectionLayerId = name
-            for idx_, lay in enumerate(nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer):                
-                lay.layerId = lay.layerId+"_"+name
-                if idx_ != 0:
-                    lay.connectionLayerId = lay.connectionLayerId+"_"+name
-            net_layers_group.extend(nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer)
-        return net_layers_group

-
-    
-
    def generate_inference_layers(self, model):
-        """
-        Generates PMML object for the inference layers of RetinaNet
-
-        Parameters
-        ----------
-        model :
-            RetinaNet model object
-
-        Returns
-        -------
-        List of Nyoka's NetworkLayer
-        """
-        inference_layers= [lay for lay in model.layers[-8:] if lay.__class__.__name__ != "Model"]
-        inference_network_layers = list()
-        for lay in inference_layers:
-            connectLayerIds=list()
-            for idx,lay_ in enumerate(lay._inbound_nodes[0].inbound_layers):
-                if hasattr(lay_,'layers'):
-                    name = lay_.layers[-1].name+"_"+self._pyramid_layers[idx]
-                else:
-                    name = lay_.name
-                connectLayerIds.append(name)
-            if lay.__class__.__name__ == 'FilterDetections':
-                connectLayerIds = connectLayerIds[:2]
-            network_layer=kerasAPI.KerasNetworkLayer(lay,"dataSet",lay.__class__.__name__, connectLayerIds)
-            network_layer.connectionLayerId = ", ".join(connectLayerIds)
-            inference_network_layers.append(network_layer)
-        return inference_network_layers

-
-    
-
    def assign_shapes(self, model, input_shape, pmml_without_shape):
-        import numpy as np
-        """
-        Assigns the shape information to each NetworkLayer of the PMML
-
-        Parameters
-        ----------
-        model :
-            RetinaNet model object
-        input_shape : tuple
-            Input shape of each image used during training
-        pmml_without_shape :
-            Generated PMML object without shape information
-
-        Returns
-        -------
-        Nyoka's PMML object
-        """
-        from keras.models import Sequential
-        from keras import backend as K
-        from keras import Model
-
-        layer_output_dict = dict()
-
-        # dummy data for shape calculation
-        sample_data = np.random.random(size=input_shape)
-        nan_index = np.isnan(sample_data)
-        sample_data[nan_index] = 0.5
-        test = np.expand_dims(sample_data, axis=0)
-        
-        # backbone and anchors
-        layers = []
-        for l in model.layers:
-            if l.__class__.__name__ == "Model":
-                break
-            layers.append(l)
-        inp = model.input
-        outputs_tens = [layer.output for layer in layers[1:]] 
-        functor = K.function([inp], outputs_tens )
-        outputs_tens.insert(0,inp)
-        layer_outs = functor([test, 1.])
-        layer_outs.insert(0, test)
-        
-        for lay, out in zip(layers, layer_outs):
-            layer_output_dict[lay.name] = out
-
-        # regression submodel
-        regression_submodel = model.layers[-8]
-        for lay in self._pyramid_layers:
-            inp = regression_submodel.get_input_at(0)
-            outputs_tens_ = [lay_.output for lay_ in regression_submodel.layers[1:]]
-            functor_ = K.function([inp], outputs_tens_ )
-            test_ = layer_output_dict[lay]
-            layer_outs_ = functor_([test_, 1.])
-            for lay_in, lay_out in zip(regression_submodel.layers[1:], layer_outs_):
-                layer_output_dict[lay_in.name+"_"+lay] = lay_out
-
-        # classification submodel
-        classification_submodel = model.layers[-4]
-        for lay in self._pyramid_layers:
-            inp = classification_submodel.get_input_at(0)
-            outputs_tens_ = [lay_.output for lay_ in classification_submodel.layers[1:]]
-            functor_ = K.function([inp], outputs_tens_ )
-            test_ = layer_output_dict[lay]
-            layer_outs_ = functor_([test_, 1.])
-            for lay_in, lay_out in zip(classification_submodel.layers[1:], layer_outs_):
-                layer_output_dict[lay_in.name+"_"+lay] = lay_out
-
-        # inference layers
-        inference_layers= [lay for lay in model.layers[-8:] if lay.__class__.__name__ != "Model"]
-        for lay in inference_layers:
-            layer_name = lay.name
-            intermediate_layer_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output)
-            layer_output_dict[layer_name] = intermediate_layer_model.predict(test)
-
-        # assign shapes
-        for net_layer in pmml_without_shape.DeepNetwork[0].NetworkLayer:
-            input_shape = None
-            if net_layer.connectionLayerId == "na":
-                input_shape = output_shape = str(layer_output_dict[net_layer.layerId].shape[1:])
-            else:
-                connected_layers = net_layer.connectionLayerId.split(", ")
-                if len(connected_layers) > 1:
-                    input_shape = []
-                    for con_lay in connected_layers:
-                        input_shape.append(str(layer_output_dict[con_lay].shape[1:]))
-                    input_shape = ", ".join(input_shape)
-                else:
-                    input_shape = str(layer_output_dict[connected_layers[0]].shape[1:])
-                if net_layer.layerType == 'FilterDetections':
-                    new_shape_lst = [0,0]
-                    for o_shape in layer_output_dict[net_layer.layerId]:
-                        o_shape = o_shape.shape[1:]
-                        if len(o_shape) == 1:
-                            shp = (o_shape[0], 1)
-                        else:
-                            shp = o_shape
-                        new_shape_lst[0] = shp[0]
-                        new_shape_lst[1] += shp[1]
-                    output_shape = str(tuple(new_shape_lst))
-                else:
-                    output_shape = str(layer_output_dict[net_layer.layerId].shape[1:])
-            net_layer.LayerParameters.inputDimension = input_shape
-            net_layer.LayerParameters.outputDimension = output_shape
-        return pmml_without_shape

-
-
    def get_output(self):
-        """
-        Generates Output for RetinaNet
-
-        Returns
-        -------
-        Nyoka's Output object
-        """
-        out_flds = []
-        out_flds.append(
-            pml.OutputField(
-                name="predicted_LabelBoxScore",
-                dataType="string",
-                feature="predictedValue",
-                Extension = [pml.Extension(extender="ADAPA", name="format", value="JSON")]
-            )
-        )
-        return pml.Output(OutputField=out_flds)

-
-
    def get_training_parameter(self):
-        """
-        Generates TrainingParameters for RetinaNet
-
-        Returns
-        -------
-        Nyoka's TrainingParameters object
-        """
-        train_param = pml.TrainingParameters(architectureName='retinanet')
-        return train_param

-
-
    def get_local_transformation(self):
-        """
-        Generates Trasformation information for RetinaNet
-
-        Returns
-        -------
-        Nyoka's LocalTransformations object
-        """
-        apply = pml.Apply(
-            function='KerasRetinaNet:getBase64StringFromBufferedInput',
-            FieldRef = [pml.FieldRef(field=self.input_format)],
-            Constant = [pml.Constant(valueOf_='tf' if self.backbone_name in ['mobilenet', 'densenet'] else 'caffe')]
-        )
-        der_fld = pml.DerivedField(
-            name="base64String",
-            optype="categorical",
-            dataType="string",
-            Apply = apply
-        )
-        return pml.LocalTransformations(DerivedField = [der_fld])

-
-    
-
    def generate_pmml(self,model,input_shape,input_format,trained_classes):
-        """
-        Generates PMML object for RetinaNet by combining all different part's PMML object
-
-        Parameters
-        ----------
-        model : 
-            RetinaNet model object
-        input_shape : tuple 
-            Shape of each training image
-        input_format : string (optional. default='image')
-            Input format to be used during inference with the PMML. Valid values are - 
-                "image" : Original image in png format
-                "encoded" : Base64 encoded string of the image
-        trained_classes : list or tuple
-            List of the classes on which the model was trained. If not provided, default(1 to 80) classes will be used
-            
-        Returns
-        -------
-        Generated nyoka's PMML object
-        """
-
-        backbone_and_anchor = self.generate_beckbone_anchors(model, input_format, trained_classes)
-        regression_submodel_layers = self.generate_submodel(model.layers[-8])
-        classification_submodel_layers = self.generate_submodel(model.layers[-4])
-        inference_layers = self.generate_inference_layers(model)
-        backbone_and_anchor.DeepNetwork[0].NetworkLayer.extend(
-            regression_submodel_layers+classification_submodel_layers+inference_layers
-        )
-        model_with_shape_info = self.assign_shapes(model, input_shape, backbone_and_anchor)
-        model_with_shape_info.DeepNetwork[0].numberOfLayers = len(model_with_shape_info.DeepNetwork[0].NetworkLayer)
-        model_with_shape_info.DeepNetwork[0].Output = self.get_output()
-        model_with_shape_info.DeepNetwork[0].TrainingParameters = self.get_training_parameter()
-        if self.input_format == 'image' and not self.script_args:
-            model_with_shape_info.DeepNetwork[0].LocalTransformations = self.get_local_transformation() 
-        model_with_shape_info.Header.description=self.description
-        self.pmml_obj = model_with_shape_info

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_modules/skl_to_pmml.html b/docs/_modules/skl_to_pmml.html index 236edefa..4d06f5c5 100644 --- a/docs/_modules/skl_to_pmml.html +++ b/docs/_modules/skl_to_pmml.html @@ -1,186 +1,93 @@ - - - - + - - - - - Scikit-Learn Exporter — Nyoka 4.2.0 documentation - - - - - - - - - - - - - - - - - - - - + + + skl_to_pmml — Nyoka 5.5.0 documentation + + + - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/statsmodels_to_pmml.html b/docs/_modules/statsmodels_to_pmml.html index 66f2af29..041d5998 100644 --- a/docs/_modules/statsmodels_to_pmml.html +++ b/docs/_modules/statsmodels_to_pmml.html @@ -1,181 +1,79 @@ - - - - - - - Statsmodels Exporter — Nyoka 4.4.0 documentation - - - - - - - - - - - - + + + statsmodels_to_pmml — Nyoka 5.5.0 documentation + + - - - + + - - - - - + + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/xgboost_to_pmml.html b/docs/_modules/xgboost_to_pmml.html index a03a3b56..a853f6b4 100644 --- a/docs/_modules/xgboost_to_pmml.html +++ b/docs/_modules/xgboost_to_pmml.html @@ -1,185 +1,93 @@ - - - - + - - - - - XGBoost Exporter — Nyoka 4.2.0 documentation - - - - - - - - - - - - - - - - - - - - + + + xgboost_to_pmml — Nyoka 5.5.0 documentation + + + - - + + + + + + - - - +
- -
- - -
- - - - - - - - - + \ No newline at end of file diff --git a/docs/_sources/PMML44.rst.txt b/docs/_sources/PMML44.rst.txt deleted file mode 100644 index 6b2dfc45..00000000 --- a/docs/_sources/PMML44.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -PMML44 module -============= - -.. automodule:: PMML44 - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/PMML44Super.rst.txt b/docs/_sources/PMML44Super.rst.txt deleted file mode 100644 index f18f439e..00000000 --- a/docs/_sources/PMML44Super.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -PMML44Super module -================== - -.. automodule:: PMML44Super - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/arima.rst.txt b/docs/_sources/arima.rst.txt deleted file mode 100644 index 474b0423..00000000 --- a/docs/_sources/arima.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -arima module -============ - -.. automodule:: arima - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/exponential_smoothing.rst.txt b/docs/_sources/exponential_smoothing.rst.txt index e9ce2e9a..35f79c84 100644 --- a/docs/_sources/exponential_smoothing.rst.txt +++ b/docs/_sources/exponential_smoothing.rst.txt @@ -2,6 +2,6 @@ exponential\_smoothing module ============================= .. automodule:: exponential_smoothing - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt index 6aa7ca02..efa639ae 100644 --- a/docs/_sources/index.rst.txt +++ b/docs/_sources/index.rst.txt @@ -1,15 +1,12 @@ -script -====== +scripts +======= .. toctree:: :maxdepth: 4 PMML44 PMML44Super - arima - constants exponential_smoothing - lag lgb_to_pmml metadata pre_process diff --git a/docs/_sources/lag.rst.txt b/docs/_sources/lag.rst.txt deleted file mode 100644 index 2d13a429..00000000 --- a/docs/_sources/lag.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -lag module -========== - -.. automodule:: lag - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/lgb_to_pmml.rst.txt b/docs/_sources/lgb_to_pmml.rst.txt index 85cfd820..612139c1 100644 --- a/docs/_sources/lgb_to_pmml.rst.txt +++ b/docs/_sources/lgb_to_pmml.rst.txt @@ -2,6 +2,6 @@ lgb\_to\_pmml module ==================== .. automodule:: lgb_to_pmml - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/metadata.rst.txt b/docs/_sources/metadata.rst.txt deleted file mode 100644 index 723dd5d2..00000000 --- a/docs/_sources/metadata.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -metadata module -=============== - -.. automodule:: metadata - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_sources/pre_process.rst.txt b/docs/_sources/pre_process.rst.txt index 52cfe847..dd08edc0 100644 --- a/docs/_sources/pre_process.rst.txt +++ b/docs/_sources/pre_process.rst.txt @@ -2,6 +2,6 @@ pre\_process module =================== .. automodule:: pre_process - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/skl_to_pmml.rst.txt b/docs/_sources/skl_to_pmml.rst.txt index 22d1fc0b..fe6bc6e4 100644 --- a/docs/_sources/skl_to_pmml.rst.txt +++ b/docs/_sources/skl_to_pmml.rst.txt @@ -2,6 +2,6 @@ skl\_to\_pmml module ==================== .. automodule:: skl_to_pmml - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/statsmodels_to_pmml.rst.txt b/docs/_sources/statsmodels_to_pmml.rst.txt index 13d8ae28..9049fcd4 100644 --- a/docs/_sources/statsmodels_to_pmml.rst.txt +++ b/docs/_sources/statsmodels_to_pmml.rst.txt @@ -2,6 +2,6 @@ statsmodels\_to\_pmml module ============================ .. automodule:: statsmodels_to_pmml - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/xgboost_to_pmml.rst.txt b/docs/_sources/xgboost_to_pmml.rst.txt index 667163a0..9531c8e3 100644 --- a/docs/_sources/xgboost_to_pmml.rst.txt +++ b/docs/_sources/xgboost_to_pmml.rst.txt @@ -2,6 +2,6 @@ xgboost\_to\_pmml module ======================== .. automodule:: xgboost_to_pmml - :members: - :undoc-members: - :show-inheritance: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/docs/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/docs/_static/ajax-loader.gif b/docs/_static/ajax-loader.gif deleted file mode 100644 index 61faf8ca..00000000 Binary files a/docs/_static/ajax-loader.gif and /dev/null differ diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 6df76b0a..7577acb1 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -15,6 +15,12 @@ div.clearer { clear: both; } +div.section::after { + display: block; + content: ''; + clear: left; +} + /* -- relbar ---------------------------------------------------------------- */ div.related { @@ -81,10 +87,26 @@ div.sphinxsidebar input { font-size: 1em; } +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + div.sphinxsidebar #searchbox input[type="text"] { - width: 170px; + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; } + img { border: 0; max-width: 100%; @@ -108,7 +130,7 @@ ul.search li a { font-weight: bold; } -ul.search li div.context { +ul.search li p.context { color: #888; margin: 2px 0 0 30px; text-align: left; @@ -199,6 +221,11 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ +div.body { + min-width: 360px; + max-width: 800px; +} + div.body p, div.body dd, div.body li, div.body blockquote { -moz-hyphens: auto; -ms-hyphens: auto; @@ -240,19 +267,25 @@ p.rubric { font-weight: bold; } -img.align-left, .figure.align-left, object.align-left { +img.align-left, figure.align-left, .figure.align-left, object.align-left { clear: left; float: left; margin-right: 1em; } -img.align-right, .figure.align-right, object.align-right { +img.align-right, figure.align-right, .figure.align-right, object.align-right { clear: right; float: right; margin-left: 1em; } -img.align-center, .figure.align-center, object.align-center { +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { display: block; margin-left: auto; margin-right: auto; @@ -266,30 +299,45 @@ img.align-center, .figure.align-center, object.align-center { text-align: center; } +.align-default { + text-align: center; +} + .align-right { text-align: right; } /* -- sidebars -------------------------------------------------------------- */ -div.sidebar { +div.sidebar, +aside.sidebar { margin: 0 0 0.5em 1em; border: 1px solid #ddb; - padding: 7px 7px 0 7px; + padding: 7px; background-color: #ffe; width: 40%; float: right; + clear: right; + overflow-x: auto; } p.sidebar-title { font-weight: bold; } +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + /* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, div.topic { border: 1px solid #ccc; - padding: 7px 7px 0 7px; + padding: 7px; margin: 10px 0 10px 0; } @@ -311,10 +359,6 @@ div.admonition dt { font-weight: bold; } -div.admonition dl { - margin-bottom: 0; -} - p.admonition-title { margin: 0px 10px 5px 0px; font-weight: bold; @@ -325,13 +369,48 @@ div.body p.centered { margin-top: 25px; } +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + /* -- tables ---------------------------------------------------------------- */ table.docutils { + margin-top: 10px; + margin-bottom: 10px; border: 0; border-collapse: collapse; } +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + table caption span.caption-number { font-style: italic; } @@ -347,10 +426,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -365,22 +440,34 @@ table.citation td { border-bottom: none; } +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + /* -- figures --------------------------------------------------------------- */ -div.figure { +div.figure, figure { margin: 0.5em; padding: 0.5em; } -div.figure p.caption { +div.figure p.caption, figcaption { padding: 0.3em; } -div.figure p.caption span.caption-number { +div.figure p.caption span.caption-number, +figcaption span.caption-number { font-style: italic; } -div.figure p.caption span.caption-text { +div.figure p.caption span.caption-text, +figcaption span.caption-text { } /* -- field list styles ----------------------------------------------------- */ @@ -405,6 +492,74 @@ table.field-list td, table.field-list th { hyphens: manual; } +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + /* -- other body styles ----------------------------------------------------- */ ol.arabic { @@ -427,11 +582,81 @@ ol.upperroman { list-style: upper-roman; } +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + dl { margin-bottom: 15px; } -dd p { +dd > :first-child { margin-top: 0px; } @@ -445,21 +670,22 @@ dd { margin-left: 30px; } -dt:target, .highlighted { - background-color: #fbe54e; +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; } -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; +dt:target, span.highlighted { + background-color: #fbe54e; } -.optional { - font-size: 1.3em; +rect.highlighted { + fill: #fbe54e; } -.sig-paren { - font-size: larger; +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; } .versionmodified { @@ -500,6 +726,13 @@ dl.glossary dt { font-style: oblique; } +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + abbr, acronym { border-bottom: dotted 1px; cursor: help; @@ -512,29 +745,69 @@ pre { overflow-y: hidden; /* fixes display issues on Chrome browsers */ } +pre, div[class*="highlight-"] { + clear: both; +} + span.pre { -moz-hyphens: none; -ms-hyphens: none; -webkit-hyphens: none; hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; } td.linenos pre { - padding: 5px 0px; border: 0; background-color: transparent; color: #aaa; } table.highlighttable { - margin-left: 0.5em; + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; } table.highlighttable td { - padding: 0 0.5em 0 0.5em; + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; } div.code-block-caption { + margin-top: 1em; padding: 2px 5px; font-size: small; } @@ -543,8 +816,14 @@ div.code-block-caption code { background-color: transparent; } -div.code-block-caption + div > div.highlight > pre { - margin-top: 0; +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ } div.code-block-caption span.caption-number { @@ -556,21 +835,7 @@ div.code-block-caption span.caption-text { } div.literal-block-wrapper { - padding: 1em 1em 0; -} - -div.literal-block-wrapper div.highlight { - margin: 0; -} - -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; + margin: 1em 0; } code.xref, a code { @@ -611,8 +876,7 @@ span.eqno { } span.eqno a.headerlink { - position: relative; - left: 0px; + position: absolute; z-index: 1; } diff --git a/docs/_static/comment-bright.png b/docs/_static/comment-bright.png deleted file mode 100644 index 15e27edb..00000000 Binary files a/docs/_static/comment-bright.png and /dev/null differ diff --git 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b/docs/_static/doctools.js @@ -2,286 +2,155 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - */ -jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s == 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return result; }; -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node) { - if (node.nodeType == 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) { - var span = document.createElement("span"); - span.className = className; - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this); - }); - } - } - return this.each(function() { - highlight(this); - }); -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated == 'undefined') - return string; - return (typeof translated == 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated == 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } }, - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + * helper function to focus on search bar */ - fixFirefoxAnchorBug : function() { - if (document.location.hash) - window.setTimeout(function() { - document.location.href += ''; - }, 10); + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * highlight the search words provided in the url in the text + * Initialise the domain index toggle buttons */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) == 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, + }; - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this == '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keyup(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); } + break; } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); \ No newline at end of file +_ready(Documentation.init); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js new file mode 100644 index 00000000..2bad5159 --- /dev/null +++ b/docs/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '5.5.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/_static/down-pressed.png b/docs/_static/down-pressed.png deleted file mode 100644 index 5756c8ca..00000000 Binary files a/docs/_static/down-pressed.png and /dev/null differ diff --git a/docs/_static/down.png b/docs/_static/down.png deleted file mode 100644 index 1b3bdad2..00000000 Binary files a/docs/_static/down.png and /dev/null differ diff --git a/docs/_static/fonts/Inconsolata-Bold.ttf b/docs/_static/fonts/Inconsolata-Bold.ttf deleted file mode 100644 index 809c1f58..00000000 Binary files a/docs/_static/fonts/Inconsolata-Bold.ttf and /dev/null differ diff --git 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b/docs/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf deleted file mode 100644 index eb52a790..00000000 Binary files a/docs/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf and /dev/null differ diff --git a/docs/_static/jquery-3.1.0.js b/docs/_static/jquery-3.1.0.js deleted file mode 100644 index f2fc2747..00000000 --- a/docs/_static/jquery-3.1.0.js +++ /dev/null @@ -1,10074 +0,0 @@ -/*eslint-disable no-unused-vars*/ -/*! - * jQuery JavaScript Library v3.1.0 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2016-07-07T21:44Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var document = window.document; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var concat = arr.concat; - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - - - - function DOMEval( code, doc ) { - doc = doc || document; - - var script = doc.createElement( "script" ); - - script.text = code; - doc.head.appendChild( script ).parentNode.removeChild( script ); - } -/* global Symbol */ -// Defining this global in .eslintrc would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.1.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }, - - // Support: Android <=4.0 only - // Make sure we trim BOM and NBSP - rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, - - // Matches dashed string for camelizing - rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g, - - // Used by jQuery.camelCase as callback to replace() - fcamelCase = function( all, letter ) { - return letter.toUpperCase(); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - return num != null ? - - // Return just the one element from the set - ( num < 0 ? this[ num + this.length ] : this[ num ] ) : - - // Return all the elements in a clean array - slice.call( this ); - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = jQuery.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && jQuery.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isArray: Array.isArray, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - nodeName: function( elem, name ) { - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.0 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-01-04 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - // Known :disabled false positives: - // IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset) - // not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Check form elements and option elements for explicit disabling - return "label" in elem && elem.disabled === disabled || - "form" in elem && elem.disabled === disabled || - - // Check non-disabled form elements for fieldset[disabled] ancestors - "form" in elem && elem.disabled === false && ( - // Support: IE6-11+ - // Ancestry is covered for us - elem.isDisabled === disabled || - - // Otherwise, assume any non-