median fs

regress_rr.py

@@ -396,7 +396,7 @@ def df_win_task(w_inds, df, i, cols):
     t0, t1 = time[w_inds][0], time[w_inds][-1]
     diff = time[w_inds[1:]] - time[w_inds[0:-1]]
 
-    fs_est = 1/np.mean(diff)
+    fs_est = 1/np.median(diff)
     if fs_est > 70 and 'acc_x' in cols: fs = IMU_FS
     elif fs_est < 70 and 'bvp' in cols: fs = PPG_FS
 
@@ -964,6 +964,85 @@ class EvalHandler():
     def save_eval_history(self):
         self.eval_hist.to_csv(self.eval_history_file, index=False)
 
+# save evaluation metrics in single file that handles the models for the
+# subject and config
+class DSPEvalHandler():
+    """
+    Handles the evaluation metric for each subject and DSP sensor.
+    ...
+
+    Attributes
+    ----------
+    y_true : numpy.ndarray
+        a numpy array of the respiration rate ground truth values from the
+        bioharness
+    y_pred : numpy.ndarray
+        a numpy array of the predicted respiration rate
+    subject : str
+        the subject in format Pilot01, S01 etc.
+    pfh : ProjectFileHandler
+        custom class detailing the directories, metafile, and configurations
+    sens_str : str
+        a string to inform what sensor was used
+    overwrite : bool
+        overwrites the evaluations (default False)
+
+    Methods
+    -------
+    load_eval_history()
+        loads the evaluation file
+    save_eval_history()
+        saves the evaluation file
+    update_eval_history()
+        updates the evaluation file using the new entry if there is no matching
+        model or configuration for the given subject
+    """
+    def __init__(self, y_true, y_pred, subject, pfh, sens_str, overwrite=False):
+        self.subject = subject
+        self.config = pfh.config
+        self.parent_directory = join(DATA_DIR, 'subject_specific')
+        self.fset_id = pfh.fset_id
+        self.sens_str = sens_str
+        self.overwrite = overwrite
+
+        self.evals = Evaluation(y_true, y_pred)
+
+        entry = {'subject': self.subject,
+                 'config_id': self.fset_id,
+                 'sens_str': self.sens_str,
+                }
+        self.entry = {**entry, **self.config, **self.evals.get_evals()}
+
+        self.eval_history_file = join(self.parent_directory,
+                                      'dsp_eval_history.csv')
+        self.eval_hist = self.load_eval_history()
+
+    def load_eval_history(self):
+        if not exists(self.eval_history_file):
+            return None
+        else:
+            return pd.read_csv(self.eval_history_file)
+
+    def update_eval_history(self):
+        eval_hist = self.eval_hist
+        if eval_hist is None:
+            eval_hist = pd.DataFrame([self.entry])
+        else:
+            index_list = eval_hist[
+                (eval_hist['subject'] == self.entry['subject']) &\
+                (eval_hist['config_id'] == self.entry['config_id']) &\
+                (eval_hist['sens_str'] == self.entry['sens_str'])
+            ].index.tolist()
+            if len(index_list) == 0:
+                print("adding new entry")
+                eval_hist = eval_hist._append(self.entry, ignore_index=True)
+            elif index_list is not None and self.overwrite:
+                eval_hist.loc[index_list[0]] = self.entry
+        self.eval_hist = eval_hist
+
+    def save_eval_history(self):
+        self.eval_hist.to_csv(self.eval_history_file, index=False)
+
 def imu_rr_dsp(subject,
                window_size=12,
                window_shift=0.2,
@@ -1049,35 +1128,39 @@ def imu_rr_dsp(subject,
                                                window_size=window_size, 
                                                window_shift=window_shift,
                                                fs=fs,
-                                               cols=['gyr_x', 'gyr_y', 'gyr_z'])
+                                               cols=['gyro_x', 'gyro_y', 'gyro_z'])
 
     acc_evals = Evaluation(acc_y_dsp_df[lbl_str], acc_dsp_df['pred'])
     gyr_evals = Evaluation(gyr_y_dsp_df[lbl_str], gyr_dsp_df['pred'])
     print("acc evals: \n", acc_evals.get_evals())
     print("gyr evals: \n", gyr_evals.get_evals())
-    plt.subplot(211)
-    plt.plot(acc_y_dsp_df[lbl_str]); plt.plot(acc_dsp_df['pred'])
-    plt.subplot(212)
-    plt.plot(gyr_y_dsp_df[lbl_str]); plt.plot(gyr_dsp_df['pred'])
-    plt.show()
-
-    # TODO
-    eval_handle = DSPEvalHandler(y_test.flatten(), preds.flatten(), subject,
-                              pfh, None, overwrite=overwrite)
-    eval_handle.update_eval_history()
-    eval_handle.save_eval_history()
+
+    acc_eval = DSPEvalHandler(acc_evals.y_true.values.flatten(),
+                              acc_evals.y_pred.values.flatten(),
+                              subject,
+                              pfh, 'acc', overwrite=overwrite)
+    acc_eval.update_eval_history()
+    acc_eval.save_eval_history()
+
+    gyr_eval = DSPEvalHandler(gyr_evals.y_true.values.flatten(),
+                              gyr_evals.y_pred.values.flatten(),
+                              subject,
+                              pfh, 'gyro', overwrite=overwrite)
+    gyr_eval.update_eval_history()
+    gyr_eval.save_eval_history()
 
     pp = PrettyPrinter()
-    pp.pprint(eval_handle.load_eval_history())
-
-    fig, ax = plt.subplots()
-    fig_title = '_'.join([mdl_str, subject]+[combi_str])
-    ax.plot(y_test)
-    ax.plot(preds)
-    ax.set_title(fig_title)
-    ax.legend([lbl_str, 'pred'])
+    pp.pprint(acc_eval.load_eval_history())
+
+    fig, ax = plt.subplots(2, 1)
+    ax[0].plot(acc_y_dsp_df[lbl_str]); plt.plot(acc_dsp_df['pred'])
+    ax[0].set_title("ACC")
+    ax[1].plot(gyr_y_dsp_df[lbl_str]); plt.plot(gyr_dsp_df['pred'])
+    ax[1].set_title("GYRO")
+    ax[1].legend([lbl_str, 'estimate'])
     fig_dir = join(project_dir, 'figures')
     if not exists(fig_dir): mkdir(fig_dir)
+    fig_title = '_'.join([subject, 'dsp'])
     fig.savefig(join(fig_dir, fig_title+".png"))
     plt.close()
 
@@ -1340,7 +1423,7 @@ def arg_parser():
                        )
     parser.add_argument("-s", '--subject', type=int,
                         default=2,
-                        choices=list(range(1,4))+[-1],
+                        choices=list(range(1,5))+[-1],
                        )
     parser.add_argument("-f", '--feature_method', type=str,
                         default='minirocket',
@@ -1371,12 +1454,18 @@ def arg_parser():
                         help='1 or 0 input, choose if standing data will be '\
                         'recorded or not'
                        )
+    parser.add_argument('--method', type=str,
+                        default='ml',
+                        help="choose between 'ml' or 'dsp' methods for"\
+                        " regression",
+                        choices=['ml', 'dsp']
+                       )
     args = parser.parse_args()
     return args
 
 if __name__ == '__main__':
     np.random.seed(100)
-    n_subject_max = 3
+    n_subject_max = 4
     args = arg_parser()
 
     # Load command line arguments
@@ -1390,13 +1479,14 @@ if __name__ == '__main__':
     overwrite      = args.overwrite
     data_input     = args.data_input
     test_standing  = args.test_standing
+    method         = args.method
 
     print(args)
     assert train_len>0,"--train_len must be an integer greater than 0"
 
     subject_pre_string = 'Pilot'
 
-    if subject > 0:
+    if subject > 0 and method == 'ml':
         subject = subject_pre_string+str(subject).zfill(2)
 
         sens_rr_model(subject,
@@ -1410,7 +1500,36 @@ if __name__ == '__main__':
                       test_standing=test_standing,
                       data_input=data_input,
                      )
-    else:
+    elif subject <= 0 and method == 'ml':
+        subjects = [subject_pre_string+str(i).zfill(2) for i in \
+                    range(2, n_subject_max+1)]
+
+        rr_func = partial(sens_rr_model,
+                          window_size=window_size,
+                          window_shift=window_shift,
+                          lbl_str=lbl_str,
+                          mdl_str=mdl_str,
+                          overwrite=overwrite,
+                          feature_method=feature_method,
+                          train_len=train_len,
+                          test_standing=test_standing,
+                          data_input=data_input,
+                         )
+
+        for subject in subjects:
+            rr_func(subject)
+
+    if subject > 0 and method == 'dsp':
+        subject = subject_pre_string+str(subject).zfill(2)
+        imu_rr_dsp(subject,
+                   window_size=window_size,
+                   window_shift=window_shift,
+                   lbl_str=lbl_str,
+                   overwrite=overwrite,
+                   train_len=train_len,
+                   test_standing=test_standing)
+
+    elif subject <= 0 and method == 'ml':
         subjects = [subject_pre_string+str(i).zfill(2) for i in \
                     range(2, n_subject_max+1)]