-
Notifications
You must be signed in to change notification settings - Fork 6
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #211 from mingujo/min-log_reg_re
Min log reg re
- Loading branch information
Showing
7 changed files
with
491 additions
and
81 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,76 @@ | ||
| """ Linear Regression on Begavioral data """ | ||
| """ | ||
| Purpose: | ||
| ----------------------------------------------------------------------------------- | ||
| We try to capture the significance of gain and loss amount condition for each subjects. | ||
| We fit the logistic regression line based on their responses on the experiment. The slope | ||
| of the fitted line illustrates the subject's sensitivity on either gain or loss amount. | ||
| import sys | ||
| sys.path.append(".././utils") | ||
| This script outputs plots for each subject and combine them into one image of subplots. | ||
| ----------------------------------------------------------------------------------- | ||
| """ | ||
|
|
||
| from __future__ import absolute_import, division, print_function | ||
| import sys, os | ||
| #TODO : later change this | ||
| sys.path.append(os.path.join(os.path.dirname(__file__), "../")) | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| import nibabel as nib | ||
| from logistic_reg import * | ||
| from organize_behavior_data import * | ||
|
|
||
|
|
||
|
|
||
| # Create the necessary directories if they do not exist | ||
| dirs = ['../../fig','../../fig/log_reg_behav'] | ||
| for d in dirs: | ||
| if not os.path.exists(d): | ||
| os.makedirs(d) | ||
|
|
||
| # Locate the different paths | ||
| #TODO: the current location for this file project-epsilon/code/scripts | ||
| project_path = '../../../' | ||
| # TODO: change it to relevant path | ||
| data_path = project_path+'data/ds005/' | ||
| subject_list = ['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16'] | ||
|
|
||
|
|
||
| fig = plt.figure() | ||
| for i,subject in enumerate(subject_list): | ||
| behav_df = load_in_dataframe(subject) | ||
| add_lambda = add_gainlossratio(behav_df) | ||
| columns_changed = organize_columns(add_lambda) | ||
| logit_pars = log_regression(columns_changed) | ||
|
|
||
| ### START TO PLOT ### | ||
| # calculate intercept and slope | ||
| intercept = -logit_pars['Intercept'] / logit_pars['gain'] | ||
| slope = -logit_pars['loss'] / logit_pars['gain'] | ||
| #fig = plt.figure(figsize = (10, 8)) | ||
| ax = fig.add_subplot(4, 4, i+1) | ||
| ax.set_title("Subject_%s_run001"%(str(i+1)), fontsize =10) | ||
| ax.set_axis_bgcolor('white') | ||
|
|
||
| # plot gain and loss for respcat = 1(decides to gamble) | ||
| l1, = ax.plot(behav_df[behav_df['respcat'] == 1].values[:,2], behav_df[behav_df['respcat'] == 1].values[:,1], '.', label = "Gamble", mfc = 'None', mec='red') | ||
|
|
||
| # plot gain and loss for respcat = 0(decides to not gamble) | ||
| l2, = ax.plot(behav_df[behav_df['respcat'] == 0].values[:,2], behav_df[behav_df['respcat'] == 0].values[:,1], '.', label = "Not gamble", mfc = 'None', mec='blue') | ||
|
|
||
| # draw regression line | ||
| ax.plot(behav_df['loss'], intercept + slope * behav_df['loss'],'-', color = 'green') | ||
|
|
||
| ax.set_xlabel('Loss ($)', fontsize =10) | ||
| ax.set_ylabel('Gain ($)', fontsize =10) | ||
| ax.set_xlim([2,23]) | ||
| ax.set_ylim([8,41]) | ||
| ax.tick_params(axis='x', labelsize=10) | ||
| ax.tick_params(axis='y', labelsize=10) | ||
|
|
||
| fig.legend((l1,l2), ('Gamble','Not Gamble'), loc = 'lower right', labelspacing = 0.5, fontsize = 10) | ||
| fig.tight_layout() | ||
| fig.subplots_adjust(top=0.90) | ||
| fig.suptitle("Fitted Logistic Regression Line (1(gamble) 0(not gamble) with gain and loss values\n", fontsize=12) | ||
| fig.savefig(dirs[1]+'/log_regression_behav_subplots.png',facecolor='white', edgecolor='white') | ||
|
|
||
|
|
||
| a=add_gainlossratio(behav_df) | ||
| b=organize_columns(a) | ||
| log_regression(b) |
Oops, something went wrong.