-
-
Notifications
You must be signed in to change notification settings - Fork 73
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
fix: template for kaggle foreset & s4e9 (#334)
* s4e9: remove onehot, reshape output * forest-cover-type-prediction: cross validation
- Loading branch information
Showing
6 changed files
with
225 additions
and
129 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
118 changes: 118 additions & 0 deletions
118
rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/train_past.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,118 @@ | ||
import importlib.util | ||
import random | ||
from pathlib import Path | ||
|
||
import numpy as np | ||
import pandas as pd | ||
from fea_share_preprocess import clean_and_impute_data, preprocess_script | ||
from scipy import stats | ||
from sklearn.metrics import accuracy_score, matthews_corrcoef | ||
|
||
# Set random seed for reproducibility | ||
SEED = 42 | ||
random.seed(SEED) | ||
np.random.seed(SEED) | ||
DIRNAME = Path(__file__).absolute().resolve().parent | ||
|
||
|
||
# support various method for metrics calculation | ||
def compute_metrics_for_classification(y_true, y_pred): | ||
"""Compute accuracy metric for classification.""" | ||
accuracy = accuracy_score(y_true, y_pred) | ||
return accuracy | ||
|
||
|
||
def compute_metrics_for_classification(y_true, y_pred): | ||
"""Compute MCC for classification.""" | ||
mcc = matthews_corrcoef(y_true, y_pred) | ||
return mcc | ||
|
||
|
||
def import_module_from_path(module_name, module_path): | ||
spec = importlib.util.spec_from_file_location(module_name, module_path) | ||
module = importlib.util.module_from_spec(spec) | ||
spec.loader.exec_module(module) | ||
return module | ||
|
||
|
||
# 1) Preprocess the data | ||
X_train, X_valid, y_train, y_valid, X_test, ids = preprocess_script() | ||
|
||
# 2) Auto feature engineering | ||
X_train_l, X_valid_l = [], [] | ||
X_test_l = [] | ||
|
||
for f in DIRNAME.glob("feature/feat*.py"): | ||
cls = import_module_from_path(f.stem, f).feature_engineering_cls() | ||
cls.fit(X_train) | ||
X_train_f = cls.transform(X_train) | ||
X_valid_f = cls.transform(X_valid) | ||
X_test_f = cls.transform(X_test) | ||
|
||
X_train_l.append(X_train_f) | ||
X_valid_l.append(X_valid_f) | ||
X_test_l.append(X_test_f) | ||
|
||
X_train = pd.concat(X_train_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_train_l))]) | ||
X_valid = pd.concat(X_valid_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_valid_l))]) | ||
X_test = pd.concat(X_test_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_test_l))]) | ||
|
||
print(X_train.shape, X_valid.shape, X_test.shape) | ||
|
||
# Handle inf and -inf values | ||
X_train, X_valid, X_test = clean_and_impute_data(X_train, X_valid, X_test) | ||
|
||
|
||
# 3) Train the model | ||
def flatten_columns(df: pd.DataFrame) -> pd.DataFrame: | ||
""" | ||
Flatten the columns of a DataFrame with MultiIndex columns, | ||
for (feature_0, a), (feature_0, b) -> feature_0_a, feature_0_b | ||
""" | ||
if df.columns.nlevels == 1: | ||
return df | ||
df.columns = ["_".join(col).strip() for col in df.columns.values] | ||
return df | ||
|
||
|
||
X_train = flatten_columns(X_train) | ||
X_valid = flatten_columns(X_valid) | ||
X_test = flatten_columns(X_test) | ||
|
||
model_l = [] # list[tuple[model, predict_func]] | ||
for f in DIRNAME.glob("model/model*.py"): | ||
m = import_module_from_path(f.stem, f) | ||
model_l.append((m.fit(X_train, y_train, X_valid, y_valid), m.predict)) | ||
|
||
# 4) Evaluate the model on the validation set | ||
y_valid_pred_l = [] | ||
for model, predict_func in model_l: | ||
y_valid_pred = predict_func(model, X_valid) | ||
y_valid_pred_l.append(y_valid_pred) | ||
print(y_valid_pred) | ||
print(y_valid_pred.shape) | ||
|
||
# 5) Ensemble | ||
# Majority vote ensemble | ||
y_valid_pred_ensemble = stats.mode(y_valid_pred_l, axis=0)[0].flatten() | ||
|
||
# Compute metrics | ||
accuracy = accuracy_score(y_valid, y_valid_pred_ensemble) | ||
print(f"final accuracy on valid set: {accuracy}") | ||
|
||
# 6) Save the validation metrics | ||
pd.Series(data=[accuracy], index=["multi-class accuracy"]).to_csv("submission_score.csv") | ||
|
||
# 7) Make predictions on the test set and save them | ||
y_test_pred_l = [] | ||
for model, predict_func in model_l: | ||
y_test_pred_l.append(predict_func(model, X_test)) | ||
|
||
# For multiclass classification, use the mode of the predictions | ||
y_test_pred = stats.mode(y_test_pred_l, axis=0)[0].flatten() + 1 | ||
|
||
|
||
submission_result = pd.DataFrame(y_test_pred, columns=["Cover_Type"]) | ||
submission_result.insert(0, "Id", ids) | ||
|
||
submission_result.to_csv("submission.csv", index=False) |
Oops, something went wrong.