Mercurial > repos > bgruening > sklearn_train_test_eval
diff ml_visualization_ex.py @ 11:caf7d2b71a48 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
| author | bgruening |
|---|---|
| date | Sat, 01 May 2021 01:47:26 +0000 |
| parents | a9e0b963b7bb |
| children | 2eb5c017958d |
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--- a/ml_visualization_ex.py Tue Apr 13 22:04:06 2021 +0000 +++ b/ml_visualization_ex.py Sat May 01 01:47:26 2021 +0000 @@ -13,10 +13,10 @@ from keras.models import model_from_json from keras.utils import plot_model from sklearn.feature_selection.base import SelectorMixin -from sklearn.metrics import auc, average_precision_score, confusion_matrix, precision_recall_curve, roc_curve +from sklearn.metrics import (auc, average_precision_score, confusion_matrix, + precision_recall_curve, roc_curve) from sklearn.pipeline import Pipeline - safe_eval = SafeEval() # plotly default colors @@ -51,7 +51,9 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label) + precision, recall, _ = precision_recall_curve( + y_true, y_score, pos_label=pos_label + ) ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) trace = go.Scatter( @@ -111,7 +113,9 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label) + precision, recall, _ = precision_recall_curve( + y_true, y_score, pos_label=pos_label + ) ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) plt.step( @@ -155,7 +159,9 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate) + fpr, tpr, _ = roc_curve( + y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate + ) roc_auc = auc(fpr, tpr) trace = go.Scatter( @@ -168,7 +174,9 @@ data.append(trace) layout = go.Layout( - xaxis=dict(title="False Positive Rate", linecolor="lightslategray", linewidth=1), + xaxis=dict( + title="False Positive Rate", linecolor="lightslategray", linewidth=1 + ), yaxis=dict(title="True Positive Rate", linecolor="lightslategray", linewidth=1), title=dict( text=title or "Receiver Operating Characteristic (ROC) Curve", @@ -204,7 +212,9 @@ os.rename("output.html", "output") -def visualize_roc_curve_matplotlib(df1, df2, pos_label, drop_intermediate=True, title=None): +def visualize_roc_curve_matplotlib( + df1, df2, pos_label, drop_intermediate=True, title=None +): """visualize roc-curve using matplotlib and output svg image""" backend = matplotlib.get_backend() if "inline" not in backend: @@ -216,7 +226,9 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate) + fpr, tpr, _ = roc_curve( + y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate + ) roc_auc = auc(fpr, tpr) plt.step( @@ -253,11 +265,15 @@ col = plot_selection[column_name]["col1"] else: col = None - _, input_df = read_columns(file_path, c=col, - c_option=column_option, - return_df=True, - sep='\t', header=header, - parse_dates=True) + _, input_df = read_columns( + file_path, + c=col, + c_option=column_option, + return_df=True, + sep="\t", + header=header, + parse_dates=True, + ) return input_df @@ -344,7 +360,9 @@ with open(infile_estimator, "rb") as estimator_handler: estimator = load_model(estimator_handler) - column_option = params["plotting_selection"]["column_selector_options"]["selected_column_selector_option"] + column_option = params["plotting_selection"]["column_selector_options"][ + "selected_column_selector_option" + ] if column_option in [ "by_index_number", "all_but_by_index_number", @@ -379,7 +397,11 @@ else: coefs = getattr(estimator, "feature_importances_", None) if coefs is None: - raise RuntimeError("The classifier does not expose " '"coef_" or "feature_importances_" ' "attributes") + raise RuntimeError( + "The classifier does not expose " + '"coef_" or "feature_importances_" ' + "attributes" + ) threshold = params["plotting_selection"]["threshold"] if threshold is not None: @@ -454,7 +476,9 @@ layout = go.Layout( xaxis=dict(title="Number of features selected"), yaxis=dict(title="Cross validation score"), - title=dict(text=title or None, x=0.5, y=0.92, xanchor="center", yanchor="top"), + title=dict( + text=title or None, x=0.5, y=0.92, xanchor="center", yanchor="top" + ), font=dict(family="sans-serif", size=11), # control backgroud colors plot_bgcolor="rgba(255,255,255,0)", @@ -548,9 +572,13 @@ elif plot_type == "classification_confusion_matrix": plot_selection = params["plotting_selection"] - input_true = get_dataframe(true_labels, plot_selection, "header_true", "column_selector_options_true") + input_true = get_dataframe( + true_labels, plot_selection, "header_true", "column_selector_options_true" + ) header_predicted = "infer" if plot_selection["header_predicted"] else None - input_predicted = pd.read_csv(predicted_labels, sep="\t", parse_dates=True, header=header_predicted) + input_predicted = pd.read_csv( + predicted_labels, sep="\t", parse_dates=True, header=header_predicted + ) true_classes = input_true.iloc[:, -1].copy() predicted_classes = input_predicted.iloc[:, -1].copy() axis_labels = list(set(true_classes))