def draw_binary(outpath=None):
_, ax = plt.subplots(figsize=(9, 6))
X_train, X_test, y_train, y_test = load_binary(split=True)
oz = PrecisionRecallCurve(RidgeClassifier(), ax=ax)
oz.fit(X_train, y_train)
oz.score(X_test, y_test)
oz.poof(outpath=outpath)
def draw_binary(outpath=None):
_, ax = plt.subplots(figsize=(9,6))
X_train, X_test, y_train, y_test = load_binary(split=True)
oz = PrecisionRecallCurve(RidgeClassifier(), ax=ax)
oz.fit(X_train, y_train)
oz.score(X_test, y_test)
oz.poof(outpath=outpath)
def precision_recall_f1(model, classes, X_train, Y_train, X_test, Y_test):
from yellowbrick.classifier import PrecisionRecallCurve
viz = PrecisionRecallCurve(model,
per_class=True,
iso_f1_curves=True,
fill_area=False,
micro=False)
viz.fit(X_train, Y_train)
viz.score(X_test, Y_test)
viz.poof()
def precision_recall(model, classes, X_train, Y_train, X_test, Y_test):
from yellowbrick.classifier import PrecisionRecallCurve
# Load the dataset and split into train/test splits
# Create the visualizer, fit, score, and poof it
viz = PrecisionRecallCurve(model)
viz.fit(X_train, Y_train)
viz.score(X_test, Y_test)
viz.poof()
def draw_multiclass(outpath=None, simple=True):
_, ax = plt.subplots(figsize=(9,6))
X_train, X_test, y_train, y_test = load_multiclass()
if simple:
oz = PrecisionRecallCurve(RandomForestClassifier(), ax=ax)
else:
oz = PrecisionRecallCurve(MultinomialNB(), ax=ax, per_class=True, iso_f1_curves=True, fill_area=False, micro=False)
oz.fit(X_train, y_train)
oz.score(X_test, y_test)
oz.poof(outpath=outpath)
def draw_multiclass(outpath=None, simple=True):
_, ax = plt.subplots(figsize=(9, 6))
X_train, X_test, y_train, y_test = load_multiclass()
if simple:
oz = PrecisionRecallCurve(RandomForestClassifier(), ax=ax)
else:
oz = PrecisionRecallCurve(MultinomialNB(),
ax=ax,
per_class=True,
iso_f1_curves=True,
fill_area=False,
micro=False)
oz.fit(X_train, y_train)
oz.score(X_test, y_test)
oz.poof(outpath=outpath)
print(classification_report(y_test, y_pred_thresh))
### Precision-Recall curve
from scikitplot.metrics import plot_precision_recall
rf_probas = rf.predict_proba(X_test)[:, 1]
plot_precision_recall(y_test, rf_probas)
from yellowbrick.classifier import PrecisionRecallCurve
viz = PrecisionRecallCurve(rf)
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.poof()
# Discimination Threshold - probability or score at which the positive class is chosen over the negative class
from yellowbrick.classifier import DiscriminationThreshold
viz = DiscriminationThreshold(rf)
viz.fit(X_train, y_train)
viz.poof()
# Average Precision
from sklearn.metrics import average_precision_score
average_precision_score(
y_test,
visualizer = ClassPredictionError(model, classes=classes)
visualizer.fit(X_fclass_train, y_train)
visualizer.score(X_fclass_test, y_test)
visualizer.poof(outpath="bag_class_errorf_classIF.png")
visualizer = DiscriminationThreshold(model)
visualizer.fit(X_fclass_train,
y_train) # Fit the training data to the visualizer
visualizer.score(X_fclass_test, y_test)
visualizer.poof(outpath="bag_descrimination_thresholdf_classIF.png")
# Create the visualizer, fit, score, and poof it
viz = PrecisionRecallCurve(model)
viz.fit(X_fclass_train, y_train)
viz.score(X_fclass_test, y_test)
viz.poof(outpath="bag_precision_recall_curvef_classIF.png")
#KNeighborsClassifier with f_classif features
model = KNeighborsClassifier()
model.fit(X_fclass_train, y_train)
visualizer = ClassificationReport(model, classes=classes)
visualizer.fit(X_fclass_train, y_train) # Fit the visualizer and the model
visualizer.score(X_fclass_test, y_test) # Evaluate the model on the test data
visualizer.poof(outpath="kneear_classification_report_fclassIF.png")
visualizer = ClassPredictionError(model, classes=classes)
visualizer.fit(X_fclass_train, y_train)
visualizer.score(X_fclass_test, y_test)
visualizer.poof(outpath="kneear_class_error_fclassIF.png")
# Run model with 4-fold cross validation. Report mean accuracy.
scores = cross_val_score(mlp, X_train, y_train, cv=4)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
# Plot ROC, AUC.
classes = ["Normal", "Pre-Ictal", "Seizure"]
visualizer = ROCAUC(mlp, classes=classes)
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
ROC_title = "ROCAUC_{}.png".format(animal_id)
g = visualizer.poof(outpath=ROC_title) # Save plot w unique title
# Plot the precision-recall curve.
viz = PrecisionRecallCurve(mlp)
viz.fit(X_train, y_train) # Fit the training data to the visualizer
viz.score(X_test, y_test) # Evaluate the model on the test data
PR_title = "PR_{}.png".format(animal_id)
viz.poof(outpath=PR_title) # Save plot w unique title
# Plot loss curve aka cost function.
loss_values = mlp.loss_curve_
plt.plot(loss_values)
plt.show()
Loss_title = "Loss_{}.png".format(animal_id)
plt.savefig(Loss_title)
sys.stdout.close()
# In[ ]:
def plot_pr(model, X_train, y_train, X_valid, y_valid):
visualizer = PrecisionRecallCurve(model)
visualizer.fit(X_train, y_train)
visualizer.score(X_valid, y_valid)
visualizer.poof()