basedLineF1Score = f.ScoreDataFrame(names, results, 'baseline_f1_Score') models = f.GetBasedModel() names, results = f.get_model_performance(X_train, y_train, models, SEED, 'accuracy') f.PlotBoxR().PlotResult(names, results) basedLineAccuracyScore = f.ScoreDataFrame(names, results, 'baseline_accuracy') # Record Scores ScoreCard = pd.concat([basedLineAccuracyScore, basedLineF1Score], axis=1) # Scaled Model on top of baseline # Standard Scalar models = f.GetScaledModel('standard') names, results = f.get_model_performance(X_train, y_train, models, SEED, 'f1_weighted') #f.PlotBoxR().PlotResult(names,results) # Record Scores scaledScoreStandard = f.ScoreDataFrame(names, results, 'standard_f1_score') ScoreCard = pd.concat([ScoreCard, scaledScoreStandard], axis=1) # Minmax scalar models = f.GetScaledModel('minmax') names, results = f.get_model_performance(X_train, y_train, models, SEED, 'f1_weighted') f.PlotBoxR().PlotResult(names, results) # Record Scores
def scaled_model(self, scoring, SEED, result_col_nm, scalar): models = f.GetScaledModel(scalar) names, results = f.get_model_performance(self.X_train, self.y_train, models, SEED, scoring) _score = f.ScoreDataFrame(names, results, result_col_nm) return _score