jgrewe/chirp_probing
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add flag to discard the roc data, only store auc

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Jan Grewe 2020-09-24 09:01:27 +02:00
parent acf981d339
commit 125516393f
1 changed files with 15 additions and 7 deletions
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22
response_discriminability.py
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@ -234,7 +234,7 @@ def get_chirp_metadata(block):
return trial_duration, dt, chirp_size, chirp_duration, chirp_times
def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name="", store_roc=False):
detection_performances = []
for contrast in all_contrasts:
@ -285,12 +285,15 @@ def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, ke
score = np.hstack((valid_distances_baseline, valid_distances_comparison))
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
auc = roc_auc_score(group, score)
detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
if store_roc:
detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "beat", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
print("\n")
return detection_performances
def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name=""):
def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005, cell_name="", store_roc=False):
detection_performances = []
for contrast in all_contrasts:
@ -361,13 +364,18 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
score = np.hstack((valid_no_other_distances, valid_self_vs_alone_distances))
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
auc = roc_auc_score(group, score)
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
if store_roc:
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "self vs soliloquy", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
group = np.hstack((baseline_temp, other_vs_baseline_temp))
score = np.hstack((valid_baseline_distances, valid_other_vs_baseline_distances))
fpr, tpr, _ = roc_curve(group, score, pos_label=1)
auc = roc_auc_score(group, score)
detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
if store_roc:
detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc, "true_positives": tpr, "false_positives": fpr})
else:
detection_performances.append({"cell": cell_name, "detection_task": "other vs quietness", "contrast": contrast, "df": df, "kernel_width": kernel_width, "auc": auc})
print("\n")
return detection_performances
@ -414,7 +422,7 @@ def plot_detection_results(data_frame, df, kernel_width, cell):
auc_ax.set_xlabel("contrast [%]")
auc_ax.set_ylim([0.25, 1.0])
auc_ax.set_ylabel("discriminability")
auc_ax.legend(ncol=2, fontsize=6)
auc_ax.legend(ncol=2, fontsize=6, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25)
auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls"--",)
fig.savefig("discrimination.pdf")