def validation_curve_classifier_alt(
path="images/validation_curve_classifier_alt.png"):
data = pd.read_csv(os.path.join(FIXTURES, "game", "game.csv"))
target = "outcome"
features = [col for col in data.columns if col != target]
X = pd.get_dummies(data[features])
y = data[target]
_, ax = plt.subplots()
cv = StratifiedKFold(4)
param_range = np.arange(3, 20, 2)
oz = ValidationCurve(
KNeighborsClassifier(),
ax=ax,
param_name="n_neighbors",
param_range=param_range,
cv=cv,
scoring="f1_weighted",
n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
def validation_curve_classifier(path="images/validation_curve_classifier.png"):
data = pd.read_csv(os.path.join(FIXTURES, "game", "game.csv"))
target = "outcome"
features = [col for col in data.columns if col != target]
X = pd.get_dummies(data[features])
y = data[target]
_, ax = plt.subplots()
cv = StratifiedKFold(12)
param_range = np.logspace(-6, -1, 12)
oz = ValidationCurve(
SVC(),
ax=ax,
param_name="gamma",
param_range=param_range,
logx=True,
cv=cv,
scoring="f1_weighted",
n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
def plot_validation_curve(final_X, final_Y):
viz = ValidationCurve(DecisionTreeClassifier(),
param_name="max_depth",
param_range=np.arange(1, 30),
cv=10,
scoring="accuracy")
viz.fit(final_X, final_Y)
viz.poof()
def validation_curve_sklearn_example(path="images/validation_curve_sklearn_example.png"):
digits = load_digits()
X, y = digits.data, digits.target
_, ax = plt.subplots()
param_range = np.logspace(-6, -1, 5)
oz = ValidationCurve(
SVC(), ax=ax, param_name="gamma", param_range=param_range,
logx=True, cv=10, scoring="accuracy", n_jobs=4
)
oz.fit(X, y)
oz.poof(outpath=path)
def draw_validation_curve(self,
param_name,
param_range,
cv,
logx=False,
scoring="accuracy",
n_jobs=5):
visualizer = ValidationCurve(self.model,
param_name=param_name,
param_range=param_range,
logx=logx,
cv=cv,
scoring=scoring,
n_jobs=n_jobs)
visualizer.fit(self.training_data, self.training_labels)
visualizer.poof()
def validation_curve_sklearn_example(
path="images/validation_curve_sklearn_example.png"):
digits = load_digits()
X, y = digits.data, digits.target
_, ax = plt.subplots()
param_range = np.logspace(-6, -1, 5)
oz = ValidationCurve(SVC(),
ax=ax,
param_name="gamma",
param_range=param_range,
logx=True,
cv=10,
scoring="accuracy",
n_jobs=4)
oz.fit(X, y)
oz.poof(outpath=path)
def validation_curve_regressor(path="images/validation_curve_regressor.png"):
data = pd.read_csv(os.path.join(FIXTURES, "energy", "energy.csv"))
targets = ["heating load", "cooling load"]
features = [col for col in data.columns if col not in targets]
X = data[features]
y = data[targets[1]]
_, ax = plt.subplots()
param_range = np.arange(1, 11)
oz = ValidationCurve(
DecisionTreeRegressor(), ax=ax, param_name="max_depth",
param_range=param_range, cv=10, scoring="r2", n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
def validation_curve_classifier_alt(path="images/validation_curve_classifier_alt.png"):
data = pd.read_csv(os.path.join(FIXTURES, "game", "game.csv"))
target = "outcome"
features = [col for col in data.columns if col != target]
X = pd.get_dummies(data[features])
y = data[target]
_, ax = plt.subplots()
cv = StratifiedKFold(4)
param_range = np.arange(3, 20, 2)
oz = ValidationCurve(
KNeighborsClassifier(), ax=ax, param_name="n_neighbors",
param_range=param_range, cv=cv, scoring="f1_weighted", n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
def validation_curve_classifier(path="images/validation_curve_classifier.png"):
data = pd.read_csv(os.path.join(FIXTURES, "game", "game.csv"))
target = "outcome"
features = [col for col in data.columns if col != target]
X = pd.get_dummies(data[features])
y = data[target]
_, ax = plt.subplots()
cv = StratifiedKFold(12)
param_range = np.logspace(-6, -1, 12)
oz = ValidationCurve(
SVC(), ax=ax, param_name="gamma", param_range=param_range,
logx=True, cv=cv, scoring="f1_weighted", n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
def validation_curve(model, X, y):
from yellowbrick.model_selection import ValidationCurve
from sklearn.model_selection import StratifiedKFold
# Create the validation curve visualizer
cv = StratifiedKFold(12)
# param_range = np.linspace(30.00, 300.00, num=50.00, dtype=np.float64)
param_range = np.logspace(30, 300, num=100, dtype=np.int32)
viz = ValidationCurve(
model,
param_name="n_estimators",
param_range=param_range,
logx=True,
cv=cv,
scoring="f1_weighted",
n_jobs=8,
)
viz.fit(X, y)
viz.poof()
def validation_curve_regressor(path="images/validation_curve_regressor.png"):
data = pd.read_csv(os.path.join(FIXTURES, "energy", "energy.csv"))
targets = ["heating load", "cooling load"]
features = [col for col in data.columns if col not in targets]
X = data[features]
y = data[targets[1]]
_, ax = plt.subplots()
param_range = np.arange(1, 11)
oz = ValidationCurve(
DecisionTreeRegressor(),
ax=ax,
param_name="max_depth",
param_range=param_range,
cv=10,
scoring="r2",
n_jobs=8,
)
oz.fit(X, y)
oz.poof(outpath=path)
plt.tight_layout()
plt.gcf().set_size_inches(10, 5)
plt.show()
# Train & Validation Curves mit yellowbricks
fig, ax = plt.subplots(figsize=(16, 9))
val_curve = ValidationCurve(
KNeighborsRegressor(),
param_name='n_neighbors',
param_range=n_neighbors,
cv=5,
scoring=rmse_score,
# n_jobs=-1,
ax=ax)
val_curve.fit(X, y)
val_curve.poof()
fig.tight_layout()
plt.show()
fig, ax = plt.subplots(figsize=(16, 9))
l_curve = LearningCurve(
KNeighborsRegressor(n_neighbors=best_k),
train_sizes=np.arange(.1, 1.01, .1),
scoring=rmse_score,
cv=5,
# n_jobs=-1,
ax=ax)
l_curve.fit(X, y)
l_curve.poof()
fig.tight_layout()
plt.show()