def predict_results():
user_id = g.user['id']
is_default_model = request.form.get("is_default_model")
if int(is_default_model):
r = UserData.get_model(0)
else:
r = UserData.get_model(user_id)
features = r['features'].split(',')
selected_file = request.form["available_files"]
df_path = USER_PATH / str(user_id) / selected_file
df = PreProcess.getDF(df_path)
is_norm = request.form.get("is_norm")
is_map = request.form.get("is_map")
if is_map == "true":
annotation_file = request.form["anno_tbl"]
annotation_table_path = UPLOAD_FOLDER.as_posix() + annotation_file
df = PreProcess.mergeDF(df_path, Path(annotation_table_path))
df = df.dropna(axis=0, subset=['Gene Symbol'])
df = PreProcess.probe2Symbol(df)
df = PreProcess.step3(df, 'sklearn', 'drop')
df = df.set_index(['Gene Symbol'])
df = df.T
elif is_norm == "true":
df = get_norm_df(df)
model_name = r['model_path_name']
e = ValidateUser.has_col(df.columns, features)
if e is not None:
return render_template("error.html", errors=e)
result = get_predicted_result_df(user_id, model_name, df[features],
is_default_model)
result = result.astype(str)
result[result == '0'] = 'Negative'
result[result == '1'] = 'Positive'
frame = {'ID': df.index, 'Predicted Result': result}
out_result = pd.DataFrame(frame)
save_path = USER_PATH / str(user_id) / "tmp" / "results.pkl"
out_result.to_pickle(save_path)
data = out_result['Predicted Result'].value_counts()
return render_template(
"modeling/predict-results.html",
tables=[out_result.to_html(classes='display" id = "table_id')],
data=data)
def predict():
user_id = g.user['id']
path = USER_PATH / str(g.user["id"])
list_names = [f for f in os.listdir(path) if os.path.isfile((path / f))]
annotation_list = []
annotation_db = UserData.get_annotation_file(g.user["id"])
for f in annotation_db:
annotation_list.append([f['file_name'], f['path']])
r = UserData.get_model(user_id)
if r['accuracy'] is None:
return redirect(url_for('modeling.index') + "?s=2")
features = r['features'].split(',')
trained_file = r['trained_file']
clasifier = r['clasifier']
accuracy = r['accuracy']
accuracy = str(round(float(accuracy), 2))
details = [features, trained_file, clasifier, accuracy]
return render_template("modeling/predict.html",
available_list=list_names,
details=details,
annotation_list=annotation_list)
