def work(confer, last_date):
out_file_name = confer.get_out_file_prefix() + ".pred.md"
out_file = open(out_file_name, "w", encoding="utf-8")
stuff_dir_name = out_file_name + ".data"
os.makedirs(stuff_dir_name, exist_ok=True)
crosser_set = CrosserSet(confer)
print("\n" + crosser_set.pred(last_date).round(4).to_html(), file=out_file)
import markdown2 as md
text = ""
with open(out_file_name, 'r', encoding='utf-8') as f:
text = f.read()
html = md.markdown(text, extras=["tables"])
out_file_html = confer.get_out_file_prefix() + ".pred.html"
with open(out_file_html, "w", encoding='utf-8') as fout:
print(html, file=fout)
from shutil import copyfile
copyfile(out_file_html, os.path.join(root, "report", "pred.html"))
def work(confer, last_date):
out_file_name = confer.get_out_file_prefix() + ".pred.md"
out_file = open(out_file_name, "w", encoding="utf-8")
stuff_dir_name = out_file_name + ".data"
os.makedirs(stuff_dir_name, exist_ok=True)
crosser_set = CrosserSet(confer)
print("\n" + crosser_set.pred(last_date).round(4).to_html(), file=out_file)
import markdown2 as md
text = ""
with open(out_file_name, 'r', encoding='utf-8') as f:
text = f.read()
html = md.markdown(text, extras=["tables"])
out_file_html = confer.get_out_file_prefix() + ".pred.html"
with open(out_file_html, "w", encoding='utf-8') as fout:
print(html, file=fout)
from shutil import copyfile
copyfile(out_file_html, os.path.join(root, "report", "pred.html"))
def work(confer):
def accurate(crosses, crossname):
print('\n\n|symset|glo_l1|sel_l1|glo_l2|sel_l2|select_len|min|max|', file=out_file)
print('|------|------|------|------|------|----------|---|---|', file=out_file)
for threshold in [2000, 1000, 500, 200, 100]:
for each in crosses:
cross = each[crossname]
df_test = pd.concat([c.df_test for c in cross])
df_test.sort_values("pred", ascending=False, inplace=True)
df_select, df_year, df_month = ana.select2( confer.score1, confer.score2,
df_test,
2, threshold)
print('|%s|%.2f|%.2f|%.2f|%.2f|%d|%.2f|%.2f|'
% (
each["symsetname"],
ana.accurate(df_test, confer.score1),
ana.accurate(df_select, confer.score1),
ana.accurate(df_test, confer.score2),
ana.accurate(df_select , confer.score2),
len(df_select),
df_select.tail(1)["pred"] if len(df_select) > 0 else 0,
df_select.head(1)["pred"] if len(df_select) > 0 else 0), file=out_file)
def top_10_decision_path(crosses, crossname):
print('|symset|glo_l1|sel_l1|glo_l2|sel_l2|select_len|min|max|', file=out_file)
print('|------|------|------|------|------|----------|---|---|', file=out_file)
for each in crosses:
cross = each[crossname]
df_test = pd.concat([c.df_test for c in cross])
df_select, df_year, df_month = ana.select2(confer.score1, confer.score2, df_test,
1, 10)
print('|%s|%.2f|%.2f|%.2f|%.2f|%d|%.2f|%.2f|'
% (
each["symsetname"],
ana.accurate(df_test, confer.score1),
ana.accurate(df_select, confer.score1),
ana.accurate(df_test, confer.score2),
ana.accurate(df_select , confer.score2),
len(df_select),
df_select.tail(1)["pred"] if len(df_select) > 0 else 0,
df_select.head(1)["pred"] if len(df_select) > 0 else 0), file=out_file)
np_feat = df_select[base.get_feat_names(df_select)].values
classifier = cross[0].classifier
for i in range(len(np_feat)):
x = np_feat[i,:]
print(x)
dot_file = os.path.join(root, "data", "cross",
'top_10_decision_path-%s-%d'
% (each["symsetname"], i))
decision_path.export_decision_path2(classifier, x, dot_file + ".dot" ,
feature_names=base.get_feat_names(df_select))
import pydot
(graph,) = pydot.graph_from_dot_file(dot_file + ".dot")
graph.write_png(dot_file + ".png")
for each in crosses:
for i in range(10):
dot_file = os.path.join(root, "data", "cross",
'top_10_decision_path-%s-%d'
% (each["symsetname"], i))
print("" % (dot_file), file=out_file)
assert isinstance(confer, MltradeConf)
out_file_name = confer.get_out_file_prefix() + ".model.md"
print(out_file_name)
out_file = open(out_file_name, "w", encoding="utf-8")
stuff_dir_name = out_file_name + ".data"
os.makedirs(stuff_dir_name, exist_ok=True)
crosser_set = CrosserSet(confer)
print("\n" + crosser_set.to_table("model").round(4).to_html(), file=out_file)
crosser_set.plot_roc("model",
os.path.join(stuff_dir_name, "model.png"))
print("\n" % (ntpath.basename(out_file_name), "model"), file=out_file)
print("\n"+ crosser_set.to_table("valid").round(4).to_html(), file=out_file)
crosser_set.plot_roc("valid",
os.path.join(stuff_dir_name, "valid.png"))
print("\n" % (ntpath.basename(out_file_name), "valid"), file=out_file)
print("## bulls...", file=out_file)
crosser_set.plot_precision_recall_bulls("model",
os.path.join(stuff_dir_name, "model_pp.png"))
print("\n" % (ntpath.basename(out_file_name), "model_pp"), file=out_file)
print("\n"+ crosser_set.to_table("valid").round(4).to_html(), file=out_file)
crosser_set.plot_precision_recall_bulls("valid",
os.path.join(stuff_dir_name, "valid_pp.png"))
print("\n" % (ntpath.basename(out_file_name), "valid_pp"), file=out_file)
#print("\n" + crosser_set.ipts_table("model").head(10).round(4).to_html(), file=out_file)
#print("\n" + crosser_set.ipts_table("model").tail(10).round(4).to_html(), file=out_file)
print("## bears...", file=out_file)
crosser_set.plot_precision_recall_bears("model",
os.path.join(stuff_dir_name, "model_pp_bears.png"))
print("\n" % (ntpath.basename(out_file_name), "model_pp_bears"), file=out_file)
print("\n"+ crosser_set.to_table("valid").round(4).to_html(), file=out_file)
crosser_set.plot_precision_recall_bears("valid",
os.path.join(stuff_dir_name, "valid_pp_bears.png"))
print("\n" % (ntpath.basename(out_file_name), "valid_pp_bears"), file=out_file)
print("\n" + crosser_set.ipts_table("model").head(10).round(4).to_html(), file=out_file)
print("\n" + crosser_set.ipts_table("model").tail(10).round(4).to_html(), file=out_file)
print("\n" + crosser_set.accurate("model").round(4).to_html(), file=out_file)
crosser_set.plot_top_precision("model",
os.path.join(stuff_dir_name, "model_tp.png"), score_name="pred")
print("\n" % (ntpath.basename(out_file_name), "model_tp"), file=out_file)
crosser_set.plot_top_precision("valid",
os.path.join(stuff_dir_name, "valid_tp.png"), score_name="pred")
print("\n" % (ntpath.basename(out_file_name), "valid_tp"), file=out_file)
crosser_set.plot_top_precision("model",
os.path.join(stuff_dir_name, "model_tp2.png"), score_name="pred2")
print("\n" % (ntpath.basename(out_file_name), "model_tp2"), file=out_file)
crosser_set.plot_top_precision("valid",
os.path.join(stuff_dir_name, "valid_tp2.png"), score_name="pred2")
print("\n" % (ntpath.basename(out_file_name), "valid_tp2"), file=out_file)
print("\n" + crosser_set.top_bulls("model").round(4).to_html(), file=out_file)
print("\n" + crosser_set.top_bulls("valid").round(4).to_html(), file=out_file)
print("\n" + crosser_set.top_bears("model").round(4).to_html(), file=out_file)
print("\n" + crosser_set.top_bears("valid").round(4).to_html(), file=out_file)
out_file.close()
import markdown2 as md
text = ""
with open(out_file_name, 'r', encoding='utf-8') as f:
text = f.read()
html = md.markdown(text, extras=["tables"])
out_file_html = confer.get_out_file_prefix() + ".model.html"
with open(out_file_html, "w", encoding='utf-8') as fout:
print(html, file=fout)
from shutil import copyfile
copyfile(out_file_html, os.path.join(root, "report", "model.html"))
