def plot_roc_auc(y_true, y_pred, label, ax=None): fpr, tpr, _ = roc_curve(y_true, y_pred) roc_curve_df = pd.DataFrame({"fpr": fpr, "tpr": tpr}).pipe( pa.DataFrameSchema({ "fpr": Column(pa.Float, Check.in_range(0, 1)), "tpr": Column(pa.Float, Check.in_range(0, 1)), }) ) return roc_curve_df.plot.line(x="fpr", y="tpr", label=label, ax=ax)
"european_american_white", "hispanic_latino", "middle_eastern", "native_american_alaskan", "race_unspecified", ] causes_of_death = [ 'asphyxiated_restrained', 'beaten_bludgeoned_with_instrument', 'burned_smoke_inhalation', 'chemical_agent_pepper_spray', 'drowned', 'drug_overdose', 'fell_from_a_height', 'gunshot', 'medical_emergency', 'other', 'stabbed', 'tasered', 'undetermined', 'unknown', 'vehicle' ] # %% training_data_schema = pa.DataFrameSchema( { # feature columns "age": Column(pa.Float, Check.in_range(0, 120), nullable=True), "gender": Column(pa.String, Check.isin(genders), nullable=True), "race": Column(pa.String, Check.isin(races), nullable=True), "cause_of_death": Column(pa.String, Check.isin(causes_of_death), nullable=True), "symptoms_of_mental_illness": Column(pa.Bool, nullable=True), # target column "disposition_accidental": Column(pa.Bool, nullable=False), }, coerce=True # <- coerce columns to the specified type ) # %% [markdown] slideshow={"slide_type": "subslide"} # #### Serialize schema to yaml format: # %% print(training_data_schema.to_yaml())