add parser argument for naming in multi model modes, minor fixes,

This commit is contained in:
René Knaebel 2017-09-05 12:40:37 +02:00
parent 1da31cc97c
commit ed4f478bad
6 changed files with 34 additions and 20 deletions

View File

@ -58,6 +58,9 @@ parser.add_argument("--domain_length", action="store", dest="domain_length",
parser.add_argument("--domain_embd", action="store", dest="domain_embedding",
default=512, type=int)
parser.add_argument("--out-prefix", action="store", dest="output_prefix",
default="", type=str)
# parser.add_argument("--queue", action="store", dest="queue_size",
# default=50, type=int)

View File

@ -75,7 +75,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
y = Conv1D(cnn_dims,
kernel_size,
activation='relu',
input_shape=(window_size, domain_features + flow_features))(merged)
input_shape=(window_size, domain_features + flow_features))(y)
# remove temporal dimension by global max pooling
y = GlobalMaxPooling1D()(y)
y = Dropout(dropout)(y)

20
run.sh
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@ -5,7 +5,7 @@ for output in client both
do
for depth in small medium
do
for mtype in inter final staggered
for mtype in inter final
do
python main.py --mode train \
@ -13,7 +13,7 @@ do
--model /tmp/rk/results/${output}_${depth}_${mtype} \
--epochs 50 \
--embd 64 \
--hidden_chaar_dims 128 \
--hidden_char_dims 128 \
--domain_embd 32 \
--batch 256 \
--balanced_weights \
@ -24,3 +24,19 @@ do
done
done
done
for depth in small medium
do
python main.py --mode train \
--train /tmp/rk/currentData.csv \
--model /tmp/rk/results/both_${depth}_inter \
--epochs 50 \
--embd 64 \
--hidden_char_dims 128 \
--domain_embd 32 \
--batch 256 \
--balanced_weights \
--model_output both \
--type inter \
--depth ${depth}
done

17
test.sh
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@ -3,17 +3,8 @@
for output in client both
do
for depth in small medium
do
for mtype in inter final staggered
do
python main.py --mode test --batch 1024 \
--model /tmp/rk/results/${output}_${depth}_${mtype} \
--test /tmp/rk/futureData.csv \
--model_output ${output} \
--type ${mtype} \
--depth ${depth}
done
done
python3 main.py --mode test --batch 1024 \
--models tm/rk/${output}_* \
--test data/futureData.csv \
--model_output ${output}
done

View File

@ -5,7 +5,7 @@ import numpy as np
from sklearn.decomposition import TruncatedSVD
from sklearn.metrics import (
auc, classification_report, confusion_matrix, fbeta_score, precision_recall_curve,
roc_auc_score, roc_curve
roc_auc_score, roc_curve, average_precision_score
)
@ -52,7 +52,9 @@ def plot_precision_recall(y, y_pred, label=""):
# fig, ax = plt.subplots(1, 1)
# ax.hold(True)
plt.plot(recall, precision, '--', label=label)
score = fbeta_score(y, y_pred.round(), 1)
# prc_ap = average_precision_score(y, y_pred)
plt.plot(recall, precision, '--', label=f"{label} - {score}")
# ax.step(recall[::-1], decreasing_max_precision, '-r')
plt.xlabel('Recall')
plt.ylabel('Precision')
@ -87,6 +89,7 @@ def plot_roc_curve(mask, prediction, label=""):
y_pred = prediction.flatten()
fpr, tpr, thresholds = roc_curve(y, y_pred)
roc_auc = auc(fpr, tpr)
plt.xscale('log')
plt.plot(fpr, tpr, label=f"{label} - {roc_auc}")
@ -154,7 +157,8 @@ def plot_embedding(domain_embedding, labels, path, dpi=600):
domain_reduced[:, 1],
c=(labels * (1, 2)).sum(1).astype(int),
cmap=plt.cm.plasma,
s=3)
s=3,
alpha=0.2)
plt.colorbar()
plt.savefig(path, dpi=dpi)