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advcl_rulebased_Results_10Features.py
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advcl_rulebased_Results_10Features.py
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import wittgenstein as lw
from sklearn.metrics import plot_confusion_matrix, precision_score, recall_score
from sklearn.model_selection import train_test_split
import utils
def draw_confusion_matrix(Clf, X, y):
titles_options = [
("Confusion matrix, without normalization", None),
("Rules Based Confusion matrix", "true"),
]
for title, normalize in titles_options:
disp = plot_confusion_matrix(Clf, X, y, cmap="Reds", normalize=normalize)
disp.ax_.set_title(title)
plt.show()
def report(results, n_top=3):
for i in range(1, n_top + 1):
candidates = np.flatnonzero(results["rank_test_score"] == i)
for candidate in candidates:
print("Model with rank: {0}".format(i))
print(
"Mean validation score: {0:.3f} (std: {1:.3f})".format(
results["mean_test_score"][candidate],
results["std_test_score"][candidate],
)
)
print("Parameters: {0}".format(results["params"][candidate]))
print("")
class_name = ("album", "type")
df = utils.load_small_tracks(buckets="discrete")
attributes = [col for col in df.columns if col != class_name]
X = df[attributes].values
y = df[class_name]
dfX = pd.get_dummies(df[[c for c in df.columns if c != class_name]], prefix_sep="=")
dfY = df[class_name]
df = pd.concat([dfX, dfY], axis=1)
print(df.info())
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=100, stratify=y
)
df_prediction_train = pd.DataFrame()
df_prediction_test = pd.DataFrame()
df_prediction_train["y_train"] = y_train
df_prediction_test["y_test"] = y_test
def SingleTracks(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
):
print("Prediction SingleTracks:")
y_train = y_train.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 0, 1, 1],
)
y_test = y_test.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 0, 1, 1],
)
ripper_clf_ST = lw.RIPPER(k=1, prune_size=0.33)
ripper_clf_ST.fit(X_train, y_train, class_feat=("album", "type"), pos_class=0)
print(ripper_clf_ST)
# Collect performance metrics
precision = ripper_clf_ST.score(X_test, y_test, precision_score)
recall = ripper_clf_ST.score(X_test, y_test, recall_score)
cond_count = ripper_clf_ST.ruleset_.count_conds()
print(ripper_clf_ST.ruleset_.out_pretty())
print(ripper_clf_ST.ruleset_)
print(f"precision: {precision} recall: {recall} conds: {cond_count}")
y_pred_train = ripper_clf_ST.predict(X_train)
y_pred_test = ripper_clf_ST.predict(X_test)
df_prediction_train["SingleTracks_y_pred_train"] = y_pred_train
df_prediction_test["SingleTracks_y_pred_test"] = y_pred_test
return df_prediction_train, df_prediction_test
def LivePerformance(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
):
print("Prediction LivePerformance:")
y_train = y_train.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 1, 0, 1],
)
y_test = y_test.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 1, 0, 1],
)
ripper_clf_LP = lw.RIPPER(k=1, prune_size=0.33)
ripper_clf_LP.fit(X_train, y_train, class_feat=("album", "type"), pos_class=0)
print(ripper_clf_LP)
# Collect performance metrics
precision = ripper_clf_LP.score(X_test, y_test, precision_score)
recall = ripper_clf_LP.score(X_test, y_test, recall_score)
cond_count = ripper_clf_LP.ruleset_.count_conds()
print(ripper_clf_LP.ruleset_.out_pretty())
print(ripper_clf_LP.ruleset_)
print(f"precision: {precision} recall: {recall} conds: {cond_count}")
y_pred_train = ripper_clf_LP.predict(X_train)
y_pred_test = ripper_clf_LP.predict(X_test)
df_prediction_train["LivePerformance_y_pred_train"] = y_pred_train
df_prediction_test["LivePerformance_y_pred_test"] = y_pred_test
return df_prediction_train, df_prediction_test
def RadioProgram(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
):
print("Prediction RadioProgram:")
y_train = y_train.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 1, 1, 0],
)
y_test = y_test.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[1, 1, 1, 0],
)
ripper_clf_RP = lw.RIPPER(k=1, prune_size=0.33)
ripper_clf_RP.fit(X_train, y_train, class_feat=("album", "type"), pos_class=0)
print(ripper_clf_RP)
# Collect performance metrics
precision = ripper_clf_RP.score(X_test, y_test, precision_score)
recall = ripper_clf_RP.score(X_test, y_test, recall_score)
cond_count = ripper_clf_RP.ruleset_.count_conds()
print(ripper_clf_RP.ruleset_.out_pretty())
print(ripper_clf_RP.ruleset_)
print(f"precision: {precision} recall: {recall} conds: {cond_count}")
y_pred_train = ripper_clf_RP.predict(X_train)
y_pred_test = ripper_clf_RP.predict(X_test)
df_prediction_train["RadioProgram_y_pred_train"] = y_pred_train
df_prediction_test["RadioProgram_y_pred_test"] = y_pred_test
return df_prediction_train, df_prediction_test
def Album(X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test):
print("Prediction Album:")
y_train = y_train.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[0, 1, 1, 1],
)
y_test = y_test.replace(
["Album", "Single Tracks", "Live Performance", "Radio Program"],
[0, 1, 1, 1],
)
ripper_clf_A = lw.RIPPER(k=1, prune_size=0.33)
ripper_clf_A.fit(X_train, y_train, class_feat=("album", "type"), pos_class=0)
print(ripper_clf_A)
# Collect performance metrics
precision = ripper_clf_A.score(X_test, y_test, precision_score)
recall = ripper_clf_A.score(X_test, y_test, recall_score)
cond_count = ripper_clf_A.ruleset_.count_conds()
print(ripper_clf_A.ruleset_.out_pretty())
print(ripper_clf_A.ruleset_)
print(f"precision: {precision} recall: {recall} conds: {cond_count}")
y_pred_train = ripper_clf_A.predict(X_train)
y_pred_test = ripper_clf_A.predict(X_test)
df_prediction_train["Album_y_pred_train"] = y_pred_train
df_prediction_test["Album_y_pred_test"] = y_pred_test
return df_prediction_train, df_prediction_test
df_prediction_train, df_prediction_test = SingleTracks(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
)
df_prediction_train, df_prediction_test = LivePerformance(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
)
df_prediction_train, df_prediction_test = RadioProgram(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
)
df_prediction_train, df_prediction_test = Album(
X_train, X_test, y_train, y_test, df_prediction_train, df_prediction_test
)
df_prediction_train.to_csv("RuleBasedResults_train_10.csv", index=False)
df_prediction_test.to_csv("RuleBasedResults_test_10.csv", index=False)