Source code for autopilot.viz.psychometric

import altair as alt
from sklearn.linear_model import LogisticRegression
from import Subject
import numpy as np
from autopilot.utils.common import coerce_discrete
import pandas as pd

[docs]def calc_psychometric(data, var_x, var_y='response'): """ Calculate a psychometric curve (logistic regression of var_y on var_x) Args: data (:class:`pandas.DataFrame`): Subject data var_x (str): name of column to use as the discriminand var_y (str): name of the column for the response, usually 'response' Returns: params (tup): parameters for logistic function """ data = data[[var_x, var_y]].copy() try: if not all(data[var_y].str.isnumeric()): data = coerce_discrete(data, var_y) except AttributeError: # if w can't use the .str accessor, then it's probs a number pass log_regress = LogisticRegression() try:[var_x], data[var_y]) except ValueError: # need to have X data be 2d[var_x]), data[var_y]) return log_regress
[docs]def plot_psychometric(subject_protocols): """ Plot psychometric curves for selected subjects, steps, and variables Typically called by :meth:`.Terminal.plot_psychometric`. Args: subject_protocols (list): A list of tuples, each with * subject_id (str) * step_name (str) * variable (str) Returns: :class:`altair.Chart` """ for subject, step, var, n_trials in subject_protocols: # load subject dataframe and subset asub = Subject(subject) sub_df = asub.get_trial_data(step) if n_trials>0: sub_df = sub_df[-n_trials:] # pdb.set_trace() sub_df = coerce_discrete(sub_df, 'response') logit = calc_psychometric(sub_df, var) # generate points to plot regression fit logit_x = np.linspace(sub_df[var].min(), sub_df[var].max(), 100).reshape(-1,1) logit_y = logit.predict_proba(logit_x) # pdb.set_trace() this_logit_df = pd.DataFrame({'x': logit_x[:,0], 'y':logit_y[:,1]}) this_logit_df['subject'] = subject this_logit_df['type'] = 'log_regression' try: logit_df = pd.concat([logit_df, this_logit_df]) except NameError: logit_df = this_logit_df # and also mean accuracy by var sub_df_sum = sub_df.groupby(var).mean().reset_index()[[var, 'response']] sub_df_sum['subject'] = subject sub_df_sum['type'] = 'responses' sub_df_sum.rename(columns={var:'x', 'response':'y'}, inplace=True) try: sum_df = pd.concat([sum_df, sub_df_sum]) except NameError: sum_df = sub_df_sum # combine dataframes combo_df = pd.concat([sum_df, logit_df]) acc_points = alt.Chart().encode( alt.X('x:Q',scale=alt.Scale(type='sqrt'), title=var), y=alt.Y('y:Q',title="mean response") ).transform_filter( alt.FieldEqualPredicate('responses', 'type') ).mark_point() log_curves = alt.Chart().encode( alt.X('x:Q',scale=alt.Scale(type='sqrt'), title=var), y=alt.Y('y:Q',title="mean response") ).transform_filter( alt.FieldEqualPredicate('log_regression', 'type') ).mark_line() combo = alt.layer(acc_points + log_curves, data=combo_df).facet(row='subject:N') #combo.sav return combo
#combo.serve() # acc_points.serve()