def experiment_summary(pkl_file):
data = read_pickle(pkl_file)
model_config = data["config"]
if "parameters" not in model_config:
model_config["parameters"] = "?"
if "train_start_date" not in data:
model_config["train_start_date"] = "01Jan1970"
else:
model_config["train_start_date"] = data["train_start_date"].strftime("%d%b%Y")
model_config["test_end_date"] = data["test_end_date"].strftime("%d%b%Y")
# model_config["features"] = data["features"]
model_config["feature_summary"] = feature_summary(model_config["features"])
prec_at = precision_at_x_percent(data["test_labels"], data["test_predictions"], x_percent=0.01)
auc_model = compute_AUC(data["test_labels"], data["test_predictions"])
num_units = len(data["test_labels"])
cm_1 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.10)
cm_2 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.15)
cm_3 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.20)
cm_4 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.25)
cm_5 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.30)
cm_6 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.40)
cm_7 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.50)
cm_8 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.60)
cm_9 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.70)
fpr = [cm_1[0, 1], cm_2[0, 1], cm_3[0, 1], cm_4[0, 1], cm_5[0, 1], cm_6[0, 1], cm_7[0, 1], cm_8[0, 1], cm_9[0, 1]]
tpr = [cm_1[1, 1], cm_2[1, 1], cm_3[1, 1], cm_4[1, 1], cm_5[1, 1], cm_6[1, 1], cm_7[1, 1], cm_8[1, 1], cm_9[1, 1]]
fnr = [cm_1[1, 0], cm_2[1, 0], cm_3[1, 0], cm_4[1, 0], cm_5[1, 0], cm_6[1, 0], cm_7[1, 0], cm_8[1, 0], cm_9[1, 0]]
tnr = [cm_1[0, 0], cm_2[0, 0], cm_3[0, 0], cm_4[0, 0], cm_5[0, 0], cm_6[0, 0], cm_7[0, 0], cm_8[0, 0], cm_9[0, 0]]
rec_1 = recall_at_x_percent(data["test_labels"], data["test_predictions"], x_percent=0.10)
rec_2 = recall_at_x_percent(data["test_labels"], data["test_predictions"], x_percent=0.15)
rec_3 = recall_at_x_percent(data["test_labels"], data["test_predictions"], x_percent=0.20)
try:
aggregation = data["aggregation"]
except:
aggregation = "No aggregated data stored"
try:
eis_baseline = data["eis_baseline"]
except:
eis_baseline = "No baseline stored"
recall = "[{}, {}, {}]".format(rec_1.round(2), rec_2.round(2), rec_3.round(2))
return Experiment(
dateutil.parser.parse(timestamp_from_path(pkl_file)),
model_config,
auc_model,
data,
fpr,
tpr,
fnr,
tnr,
recall,
aggregation,
eis_baseline,
)
def experiment_summary(pkl_file):
data = read_pickle(pkl_file)
model_config = data["config"]
if "parameters" not in model_config:
model_config["parameters"] = "?"
if "train_start_date" not in data:
model_config["train_start_date"] = "01Jan1970"
else:
model_config["train_start_date"] = data["train_start_date"].strftime("%d%b%Y")
model_config["test_end_date"] = data["test_end_date"].strftime("%d%b%Y")
# model_config["features"] = data["features"]
model_config["feature_summary"] = feature_summary(model_config["features"])
prec_at = precision_at_x_percent(
data["test_labels"], data["test_predictions"],
x_percent=0.01)
auc_model = compute_AUC(data["test_labels"], data["test_predictions"])
num_units = len(data["test_labels"])
cm_1 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.10)
cm_2 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.15)
cm_3 = fpr_tpr(data["test_labels"], data["test_predictions"], 0.20)
fpr = [cm_1[0, 1], cm_2[0, 1], cm_3[0, 1]]
tpr = [cm_1[1, 1], cm_2[1, 1], cm_3[1, 1]]
fnr = [cm_1[1, 0], cm_2[1, 0], cm_3[1, 0]]
rec_1 = recall_at_x_percent(
data["test_labels"], data["test_predictions"],
x_percent=0.10)
rec_2 = recall_at_x_percent(
data["test_labels"], data["test_predictions"],
x_percent=0.15)
rec_3 = recall_at_x_percent(
data["test_labels"], data["test_predictions"],
x_percent=0.20)
recall = "[{}, {}, {}]".format(rec_1.round(2), rec_2.round(2), rec_3.round(2))
return Experiment(dateutil.parser.parse(timestamp_from_path(pkl_file)),
model_config,
auc_model,
data, fpr, tpr, fnr, recall)
def experiment_summary(pkl_file):
data = read_pickle(pkl_file)
model_config = data["config"]
if "parameters" not in model_config:
model_config["parameters"] = "?"
if "train_start_date" not in data:
model_config["train_start_date"] = "01Jan1970"
else:
model_config["train_start_date"] = data["train_start_date"].strftime(
"%d%b%Y")
model_config["test_end_date"] = data["test_end_date"].strftime("%d%b%Y")
# model_config["features"] = data["features"]
model_config["feature_summary"] = feature_summary(model_config["features"])
prec_at = precision_at_x_percent(data["test_labels"],
data["test_predictions"],
x_percent=0.01)
auc_model = compute_AUC(data["test_labels"], data["test_predictions"])
num_units = len(data["test_labels"])
threshold_levels = []
fpr, tpr, fnr, tnr = {}, {}, {}, {}
for each_threshold in sorted(list(data["eis_baseline"].keys())):
threshold_levels.append(each_threshold)
fpr.update({
each_threshold:
data["eis_baseline"][each_threshold]["dsapp"][0, 1]
})
tpr.update({
each_threshold:
data["eis_baseline"][each_threshold]["dsapp"][1, 1]
})
fnr.update({
each_threshold:
data["eis_baseline"][each_threshold]["dsapp"][1, 0]
})
tnr.update({
each_threshold:
data["eis_baseline"][each_threshold]["dsapp"][0, 0]
})
eis_baseline = data["eis_baseline"][each_threshold]["eis"]
rec_list = []
for rec_threshold in [10., 15., 20.]:
rec_list.append(
recall_at_x_percent(data["test_labels"],
data["test_predictions"],
x_percent=rec_threshold / 100.))
try:
aggregation = data["aggregation"]
except:
aggregation = "No aggregated data stored"
recall = "[{}, {}, {}]".format(rec_list[0].round(2), rec_list[1].round(2),
rec_list[2].round(2))
return Experiment(dateutil.parser.parse(timestamp_from_path(pkl_file)),
model_config, auc_model, data, fpr, tpr, fnr, tnr,
recall, aggregation, eis_baseline, threshold_levels)