File size: 7,181 Bytes
0887820 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | import pandas as pd
dataset_names = ["1-munchkin", "2-chinchilla",
"3-vicheanmas", "4-scottishfold"]
academic_dataset_names = ["Dataset 1", "Dataset 3", "Dataset 2", "Dataset 4"]
learning_rate = [0.005, 0.01, 0.05, 0.1, 0.5]
overall_accuracy_df = pd.DataFrame()
model_learning_rates = ["1e-07", "1e-08"]
iteration = 100
modifiers = [
"",
"_remove_outlier",
"_augmented",
"_remove_outlier_augmented",
"_early_stopping",
"_remove_outlier_early_stopping",
"_augmented_early_stopping",
"_remove_outlier_augmented_early_stopping",
]
displayed_modifiers = [
"Original",
"Remove Outlier",
"Augmentation",
"Remove Outlier + Augmentation",
"Early Stopping",
"Remove Outlier + Early Stopping",
"Augmentation + Early Stopping",
"Remove Outlier + Augmentation + Early Stopping",
]
def find_overall_accuracy(round_number=3,
model_version=1):
overall_accuracy_df = pd.DataFrame()
for dataset_name in dataset_names:
for round_no in range(round_number):
for modifer in modifiers:
accuracy_list = []
# Find Best Accuracy
# For Each Model Learning Rate
for model_learning_rate in model_learning_rates:
directory_name = f"result/{dataset_name}/{dataset_name}_{model_version}_{model_learning_rate}{modifer}"
# For Each Learning Rate
for lr in learning_rate:
datafile = f"{directory_name}/results/{lr}_round_error.csv"
result = pd.read_csv(datafile)
result_with_this_round = result[result["round"]
== round_no]
result_with_this_round = result_with_this_round.iloc[0]
# Find the last iteration which is early stopping
epoch_error_file = (
f"{directory_name}/round{round_no+1}/{iteration}-{lr}.csv"
)
epoch_error = pd.read_csv(epoch_error_file)
# Find Last Record
epoch_error = epoch_error.iloc[-1]
# Find last record epoch
last_epoch = epoch_error["epoch"]
accuracy_payload = {
"type": "training",
"learning_rate": lr,
"model_learning_rate": model_learning_rate,
"rmspe": result_with_this_round["error_percent"],
"last_epoch": last_epoch,
}
accuracy_list.append(accuracy_payload)
accuracy_payload = {
"type": "validate",
"learning_rate": lr,
"model_learning_rate": model_learning_rate,
"rmspe": result_with_this_round["validate_error_percent"],
"last_epoch": last_epoch,
}
accuracy_list.append(accuracy_payload)
accuracy_list = pd.DataFrame(accuracy_list)
best_training = (
accuracy_list[accuracy_list["type"] == "training"]
.sort_values(by="rmspe")
.iloc[0]
)
best_validate = (
accuracy_list[accuracy_list["type"] == "validate"]
.sort_values(by="rmspe")
.iloc[0]
)
# Find Data Variation For Training
if "augmented" in modifer:
train_data_variation = pd.read_csv(
f"{directory_name}/train_data_variation_after_augmented_{round_no}.csv"
)
else:
train_data_variation = pd.read_csv(
f"{directory_name}/train_data_variation_{round_no}.csv"
)
train_cost_data = train_data_variation[
train_data_variation["data"] == "Total Cost"
]
train_cost_data = train_cost_data.iloc[0]
payload = {
"dataset": dataset_name,
"round_no": round_no,
"modifer": modifer,
"type": "training",
"cv": train_cost_data["variation"],
"iqr": train_cost_data["iqr"],
"min": train_cost_data["min"],
"max": train_cost_data["max"],
"mean": train_cost_data["mean"],
"minimum_error": best_training["rmspe"],
"best_learning_rate": best_training["learning_rate"],
"best_model_learning_rate": best_training["model_learning_rate"],
"last_epoch": best_training["last_epoch"],
}
overall_accuracy_df = pd.concat(
[overall_accuracy_df, pd.DataFrame(payload, index=[0])],
ignore_index=True,
)
# Find Data Variation For Validate
validate_data_variation = pd.read_csv(
f"{directory_name}/validate_data_variation_{round_no}.csv"
)
validate_cost_data = validate_data_variation[
validate_data_variation["data"] == "Total Cost"
]
validate_cost_data = validate_cost_data.iloc[0]
payload = {
"dataset": dataset_name,
"round_no": round_no,
"modifer": modifer,
"type": "validate",
"cv": validate_cost_data["variation"],
"iqr": validate_cost_data["iqr"],
"min": validate_cost_data["min"],
"max": validate_cost_data["max"],
"mean": validate_cost_data["mean"],
"minimum_error": best_validate["rmspe"],
"best_learning_rate": best_training["learning_rate"],
"best_model_learning_rate": best_training["model_learning_rate"],
"last_epoch": best_training["last_epoch"],
}
overall_accuracy_df = pd.concat(
[overall_accuracy_df, pd.DataFrame(payload, index=[0])],
ignore_index=True,
)
# Post Processing
overall_accuracy_df["remove_outlier"] = overall_accuracy_df["modifer"].apply(
lambda x: 1 if "_remove_outlier" in x else 0
)
overall_accuracy_df["augmented"] = overall_accuracy_df["modifer"].apply(
lambda x: 1 if "_augmented" in x else 0
)
overall_accuracy_df["early_stopping"] = overall_accuracy_df["modifer"].apply(
lambda x: 1 if "_early_stopping" in x else 0
)
overall_accuracy_df.to_csv("result/overall_accuracy.csv", index=False)
|