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Update visualization codes

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D-X-Y 2021-04-12 15:42:43 +08:00
parent 5f2ba0a8e7
commit c82c7e9f3f
9 changed files with 503 additions and 16 deletions
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1
.gitignore vendored
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@ -133,3 +133,4 @@ outputs
pytest_cache pytest_cache
*.pkl *.pkl
*.pth

2
exps/trading/baselines.py
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@ -64,7 +64,7 @@ def extend_transformer_settings(alg2configs, name):
config = copy.deepcopy(alg2configs[name]) config = copy.deepcopy(alg2configs[name])
for i in range(1, 9): for i in range(1, 9):
for j in (6, 12, 24, 32, 48, 64): for j in (6, 12, 24, 32, 48, 64):
for k1 in (0, 0.1, 0.2, 0.3): for k1 in (0, 0.05, 0.1, 0.2, 0.3):
for k2 in (0, 0.1): for k2 in (0, 0.1):
alg2configs[ alg2configs[
name + "-{:}x{:}-drop{:}_{:}".format(i, j, k1, k2) name + "-{:}x{:}-drop{:}_{:}".format(i, j, k1, k2)

6
exps/trading/organize_results.py
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@ -22,6 +22,7 @@ from qlib.workflow import R
from utils.qlib_utils import QResult from utils.qlib_utils import QResult
def compare_results( def compare_results(
heads, values, names, space=10, separate="& ", verbose=True, sort_key=False heads, values, names, space=10, separate="& ", verbose=True, sort_key=False
): ):
@ -69,7 +70,10 @@ def query_info(save_dir, verbose, name_filter, key_map):
for idx, (key, experiment) in enumerate(experiments.items()): for idx, (key, experiment) in enumerate(experiments.items()):
if experiment.id == "0": if experiment.id == "0":
continue continue
if name_filter is not None and re.fullmatch(name_filter, experiment.name) is None: if (
name_filter is not None
and re.fullmatch(name_filter, experiment.name) is None
):
continue continue
recorders = experiment.list_recorders() recorders = experiment.list_recorders()
recorders, not_finished = filter_finished(recorders) recorders, not_finished = filter_finished(recorders)

21
lib/utils/qlib_utils.py
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@ -1,3 +1,4 @@
import os
import numpy as np import numpy as np
from typing import List, Text from typing import List, Text
from collections import defaultdict, OrderedDict from collections import defaultdict, OrderedDict
@ -10,6 +11,7 @@ class QResult:
self._result = defaultdict(list) self._result = defaultdict(list)
self._name = name self._name = name
self._recorder_paths = [] self._recorder_paths = []
self._date2ICs = []
def append(self, key, value): def append(self, key, value):
self._result[key].append(value) self._result[key].append(value)
@ -17,6 +19,25 @@ class QResult:
def append_path(self, xpath): def append_path(self, xpath):
self._recorder_paths.append(xpath) self._recorder_paths.append(xpath)
def append_date2ICs(self, date2IC):
if self._date2ICs: # not empty
keys = sorted(list(date2IC.keys()))
pre_keys = sorted(list(self._date2ICs[0].keys()))
assert len(keys) == len(pre_keys)
for i, (x, y) in enumerate(zip(keys, pre_keys)):
assert x == y, "[{:}] {:} vs {:}".format(i, x, y)
self._date2ICs.append(date2IC)
def find_all_dates(self):
dates = self._date2ICs[-1].keys()
return sorted(list(dates))
def get_IC_by_date(self, date, scale=1.0):
values = []
for date2IC in self._date2ICs:
values.append(date2IC[date] * scale)
return float(np.mean(values)), float(np.std(values))
@property @property
def name(self): def name(self):
return self._name return self._name

11
notebooks/TOT/ES-Model-DC.ipynb
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@ -18,10 +18,10 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[68147:MainThread](2021-04-12 13:09:24,409) INFO - qlib.Initialization - [config.py:276] - default_conf: client.\n", "[70148:MainThread](2021-04-12 13:23:30,262) INFO - qlib.Initialization - [config.py:276] - default_conf: client.\n",
"[68147:MainThread](2021-04-12 13:09:24,411) WARNING - qlib.Initialization - [config.py:291] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n", "[70148:MainThread](2021-04-12 13:23:30,266) WARNING - qlib.Initialization - [config.py:291] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n",
"[68147:MainThread](2021-04-12 13:09:24,414) INFO - qlib.Initialization - [__init__.py:46] - qlib successfully initialized based on client settings.\n", "[70148:MainThread](2021-04-12 13:23:30,269) INFO - qlib.Initialization - [__init__.py:46] - qlib successfully initialized based on client settings.\n",
"[68147:MainThread](2021-04-12 13:09:24,417) INFO - qlib.Initialization - [__init__.py:47] - data_path=/Users/xuanyidong/.qlib/qlib_data/cn_data\n" "[70148:MainThread](2021-04-12 13:23:30,271) INFO - qlib.Initialization - [__init__.py:47] - data_path=/Users/xuanyidong/.qlib/qlib_data/cn_data\n"
] ]
} }
], ],
@ -142,7 +142,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[68147:MainThread](2021-04-12 13:09:25,066) INFO - qlib.workflow - [expm.py:290] - <mlflow.tracking.client.MlflowClient object at 0x7fd449277a30>\n" "[70148:MainThread](2021-04-12 13:23:31,137) INFO - qlib.workflow - [expm.py:290] - <mlflow.tracking.client.MlflowClient object at 0x7f8c4a47efa0>\n"
] ]
}, },
{ {
@ -233,6 +233,7 @@
" cmap=cm.Spectral, linewidth=0.2, antialiased=True)\n", " cmap=cm.Spectral, linewidth=0.2, antialiased=True)\n",
" cur_ax.set_xticks(raw_depths)\n", " cur_ax.set_xticks(raw_depths)\n",
" cur_ax.set_yticks(raw_channels)\n", " cur_ax.set_yticks(raw_channels)\n",
" cur_ax.set_zticks(np.arange(4, 11, 2))\n",
" cur_ax.set_xlabel(\"#depth\", fontsize=LabelSize)\n", " cur_ax.set_xlabel(\"#depth\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"#channels\", fontsize=LabelSize)\n", " cur_ax.set_ylabel(\"#channels\", fontsize=LabelSize)\n",
" cur_ax.set_zlabel(\"{:} IC (%)\".format('training' if train_or_test else 'validation'), fontsize=LabelSize)\n", " cur_ax.set_zlabel(\"{:} IC (%)\".format('training' if train_or_test else 'validation'), fontsize=LabelSize)\n",

19
notebooks/TOT/ES-Model-Drop.ipynb
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@ -18,10 +18,10 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[64660:MainThread](2021-04-11 23:57:38,079) INFO - qlib.Initialization - [config.py:276] - default_conf: client.\n", "[70363:MainThread](2021-04-12 13:25:01,065) INFO - qlib.Initialization - [config.py:276] - default_conf: client.\n",
"[64660:MainThread](2021-04-11 23:57:38,081) WARNING - qlib.Initialization - [config.py:291] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n", "[70363:MainThread](2021-04-12 13:25:01,069) WARNING - qlib.Initialization - [config.py:291] - redis connection failed(host=127.0.0.1 port=6379), cache will not be used!\n",
"[64660:MainThread](2021-04-11 23:57:38,083) INFO - qlib.Initialization - [__init__.py:46] - qlib successfully initialized based on client settings.\n", "[70363:MainThread](2021-04-12 13:25:01,085) INFO - qlib.Initialization - [__init__.py:46] - qlib successfully initialized based on client settings.\n",
"[64660:MainThread](2021-04-11 23:57:38,084) INFO - qlib.Initialization - [__init__.py:47] - data_path=/Users/xuanyidong/.qlib/qlib_data/cn_data\n" "[70363:MainThread](2021-04-12 13:25:01,092) INFO - qlib.Initialization - [__init__.py:47] - data_path=/Users/xuanyidong/.qlib/qlib_data/cn_data\n"
] ]
} }
], ],
@ -142,7 +142,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[64660:MainThread](2021-04-11 23:57:38,469) INFO - qlib.workflow - [expm.py:290] - <mlflow.tracking.client.MlflowClient object at 0x7fba2bc7df70>\n" "[70363:MainThread](2021-04-12 13:25:01,647) INFO - qlib.workflow - [expm.py:290] - <mlflow.tracking.client.MlflowClient object at 0x7fa920e56820>\n"
] ]
}, },
{ {
@ -182,7 +182,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 8,
"id": "supreme-basis", "id": "supreme-basis",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -204,7 +204,7 @@
" \n", " \n",
" dpi, width, height = 200, 4000, 2000\n", " dpi, width, height = 200, 4000, 2000\n",
" figsize = width / float(dpi), height / float(dpi)\n", " figsize = width / float(dpi), height / float(dpi)\n",
" LabelSize, LegendFontsize = 22, 18\n", " LabelSize, LegendFontsize = 22, 22\n",
" font_gap = 5\n", " font_gap = 5\n",
" colors = ['k', 'r']\n", " colors = ['k', 'r']\n",
" markers = ['*', 'o']\n", " markers = ['*', 'o']\n",
@ -227,6 +227,7 @@
" cur_ax.scatter(x_values, y_values,\n", " cur_ax.scatter(x_values, y_values,\n",
" marker=markers[idx], s=3, c=colors[idx], alpha=0.9,\n", " marker=markers[idx], s=3, c=colors[idx], alpha=0.9,\n",
" label=legend)\n", " label=legend)\n",
" cur_ax.set_yticks(np.arange(4, 11, 2))\n",
" cur_ax.set_xlabel(\"sorted architectures\", fontsize=LabelSize)\n", " cur_ax.set_xlabel(\"sorted architectures\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"{:} IC (%)\".format('training' if train_or_test else 'validation'), fontsize=LabelSize)\n", " cur_ax.set_ylabel(\"{:} IC (%)\".format('training' if train_or_test else 'validation'), fontsize=LabelSize)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n", " for tick in cur_ax.xaxis.get_major_ticks():\n",
@ -246,7 +247,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 9,
"id": "shared-envelope", "id": "shared-envelope",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -254,7 +255,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"{'TSF-8x6', 'TSF-6x6', 'TSF-4x24', 'TSF-3x32', 'TSF-7x6', 'TSF-4x12', 'TSF-2x12', 'TSF-1x24', 'TSF-1x32', 'TSF-6x32', 'TSF-7x48', 'TSF-4x6', 'TSF-5x32', 'TSF-6x24', 'TSF-8x24', 'TSF-5x6', 'TSF-3x24', 'TSF-6x12', 'TSF-3x12', 'TSF-5x64', 'TSF-5x12', 'TSF-7x32', 'TSF-6x48', 'TSF-3x64', 'TSF-5x48', 'TSF-7x24', 'TSF-4x32', 'TSF-4x64', 'TSF-2x64', 'TSF-8x12', 'TSF-7x64', 'TSF-3x6', 'TSF-1x6', 'TSF-8x64', 'TSF-2x6', 'TSF-6x64', 'TSF-7x12', 'TSF-2x24', 'TSF-8x48', 'TSF-1x64', 'TSF-4x48', 'TSF-8x32', 'TSF-2x48', 'TSF-1x12', 'TSF-5x24', 'TSF-3x48', 'TSF-2x32', 'TSF-1x48'}\n", "{'TSF-3x48', 'TSF-2x64', 'TSF-2x12', 'TSF-8x48', 'TSF-6x32', 'TSF-4x48', 'TSF-8x6', 'TSF-4x6', 'TSF-2x32', 'TSF-5x12', 'TSF-5x64', 'TSF-1x64', 'TSF-2x24', 'TSF-8x24', 'TSF-4x12', 'TSF-6x12', 'TSF-1x32', 'TSF-5x32', 'TSF-3x24', 'TSF-8x12', 'TSF-5x48', 'TSF-6x64', 'TSF-7x64', 'TSF-7x48', 'TSF-1x6', 'TSF-2x48', 'TSF-7x24', 'TSF-3x32', 'TSF-1x24', 'TSF-4x64', 'TSF-3x12', 'TSF-8x64', 'TSF-4x32', 'TSF-5x6', 'TSF-7x6', 'TSF-7x12', 'TSF-3x6', 'TSF-4x24', 'TSF-6x48', 'TSF-6x6', 'TSF-1x48', 'TSF-1x12', 'TSF-7x32', 'TSF-5x24', 'TSF-2x6', 'TSF-6x24', 'TSF-3x64', 'TSF-8x32'}\n",
"The Desktop is at: /Users/xuanyidong/Desktop\n", "The Desktop is at: /Users/xuanyidong/Desktop\n",
"There are 104 qlib-results\n" "There are 104 qlib-results\n"
] ]

208
notebooks/TOT/Time-Curve.ipynb Normal file
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@ -0,0 +1,208 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "afraid-minutes",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The root path: /Users/xuanyidong/Desktop/AutoDL-Projects\n",
"The library path: /Users/xuanyidong/Desktop/AutoDL-Projects/lib\n"
]
}
],
"source": [
"import os\n",
"import re\n",
"import sys\n",
"import torch\n",
"import pprint\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from scipy.interpolate import make_interp_spline\n",
"\n",
"__file__ = os.path.dirname(os.path.realpath(\"__file__\"))\n",
"root_dir = (Path(__file__).parent / \"..\").resolve()\n",
"lib_dir = (root_dir / \"lib\").resolve()\n",
"print(\"The root path: {:}\".format(root_dir))\n",
"print(\"The library path: {:}\".format(lib_dir))\n",
"assert lib_dir.exists(), \"{:} does not exist\".format(lib_dir)\n",
"if str(lib_dir) not in sys.path:\n",
" sys.path.insert(0, str(lib_dir))\n",
"from utils.qlib_utils import QResult"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "continental-drain",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TSF-2x24-drop0_0s2013-01-01\n",
"TSF-2x24-drop0_0s2012-01-01\n",
"TSF-2x24-drop0_0s2008-01-01\n",
"TSF-2x24-drop0_0s2009-01-01\n",
"TSF-2x24-drop0_0s2010-01-01\n",
"TSF-2x24-drop0_0s2011-01-01\n",
"TSF-2x24-drop0_0s2008-07-01\n",
"TSF-2x24-drop0_0s2009-07-01\n",
"There are 3011 dates\n",
"Dates: 2008-01-02 2008-01-03\n"
]
}
],
"source": [
"qresults = torch.load(os.path.join(root_dir, 'notebooks', 'TOT', 'temp-time-x.pth'))\n",
"for qresult in qresults:\n",
" print(qresult.name)\n",
"all_dates = set()\n",
"for qresult in qresults:\n",
" dates = qresult.find_all_dates()\n",
" for date in dates:\n",
" all_dates.add(date)\n",
"all_dates = sorted(list(all_dates))\n",
"print('There are {:} dates'.format(len(all_dates)))\n",
"print('Dates: {:} {:}'.format(all_dates[0], all_dates[1]))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "intimate-approval",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"from matplotlib import cm\n",
"matplotlib.use(\"agg\")\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "supreme-basis",
"metadata": {},
"outputs": [],
"source": [
"def vis_time_curve(qresults, dates, use_original, save_path):\n",
" save_dir = (save_path / '..').resolve()\n",
" save_dir.mkdir(parents=True, exist_ok=True)\n",
" print('There are {:} qlib-results'.format(len(qresults)))\n",
" \n",
" dpi, width, height = 200, 5000, 2000\n",
" figsize = width / float(dpi), height / float(dpi)\n",
" LabelSize, LegendFontsize = 22, 12\n",
" font_gap = 5\n",
" linestyles = ['-', '--']\n",
" colors = ['k', 'r']\n",
" \n",
" fig = plt.figure(figsize=figsize)\n",
" cur_ax = fig.add_subplot(1, 1, 1)\n",
" for idx, qresult in enumerate(qresults):\n",
" print('Visualize [{:}] -- {:}'.format(idx, qresult.name))\n",
" x_axis, y_axis = [], []\n",
" for idate, date in enumerate(dates):\n",
" if date in qresult._date2ICs[-1]:\n",
" mean, std = qresult.get_IC_by_date(date, 100)\n",
" if not np.isnan(mean):\n",
" x_axis.append(idate)\n",
" y_axis.append(mean)\n",
" x_axis, y_axis = np.array(x_axis), np.array(y_axis)\n",
" if use_original:\n",
" cur_ax.plot(x_axis, y_axis, linewidth=1, color=colors[idx], linestyle=linestyles[idx])\n",
" else:\n",
" xnew = np.linspace(x_axis.min(), x_axis.max(), 200)\n",
" spl = make_interp_spline(x_axis, y_axis, k=5)\n",
" ynew = spl(xnew)\n",
" cur_ax.plot(xnew, ynew, linewidth=2, color=colors[idx], linestyle=linestyles[idx])\n",
" \n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" cur_ax.set_ylabel(\"IC (%)\", fontsize=LabelSize)\n",
" fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
" plt.close(\"all\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "shared-envelope",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Desktop is at: /Users/xuanyidong/Desktop\n",
"There are 2 qlib-results\n",
"Visualize [0] -- TSF-2x24-drop0_0s2008-01-01\n",
"Visualize [1] -- TSF-2x24-drop0_0s2009-07-01\n",
"There are 2 qlib-results\n",
"Visualize [0] -- TSF-2x24-drop0_0s2008-01-01\n",
"Visualize [1] -- TSF-2x24-drop0_0s2009-07-01\n"
]
}
],
"source": [
"# Visualization\n",
"home_dir = Path.home()\n",
"desktop_dir = home_dir / 'Desktop'\n",
"print('The Desktop is at: {:}'.format(desktop_dir))\n",
"\n",
"vis_time_curve(\n",
" (qresults[2], qresults[-1]),\n",
" all_dates,\n",
" True,\n",
" desktop_dir / 'es_csi300_time_curve.pdf')\n",
"\n",
"vis_time_curve(\n",
" (qresults[2], qresults[-1]),\n",
" all_dates,\n",
" False,\n",
" desktop_dir / 'es_csi300_time_curve-inter.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "exempt-stable",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

128
notebooks/TOT/synthetic.ipynb Normal file
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@ -0,0 +1,128 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "filled-multiple",
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# %matplotlib notebook\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import matplotlib\n",
"from matplotlib import cm\n",
"matplotlib.use(\"agg\")\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as ticker"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "supreme-basis",
"metadata": {},
"outputs": [],
"source": [
"def visualize_syn(save_path):\n",
" save_dir = (save_path / '..').resolve()\n",
" save_dir.mkdir(parents=True, exist_ok=True)\n",
" \n",
" dpi, width, height = 50, 2000, 1000\n",
" figsize = width / float(dpi), height / float(dpi)\n",
" LabelSize, font_gap = 30, 4\n",
" \n",
" fig = plt.figure(figsize=figsize)\n",
" \n",
" times = np.arange(0, np.pi * 100, 0.1)\n",
" num = len(times)\n",
" x = []\n",
" for i in range(num):\n",
" scale = (i + 1.) / num * 4\n",
" value = times[i] * scale\n",
" x.append(np.sin(value) * (1.3 - scale))\n",
" x = np.array(x)\n",
" y = np.cos( x * x - 0.3 * x )\n",
" \n",
" cur_ax = fig.add_subplot(2, 1, 1)\n",
" cur_ax.plot(times, x)\n",
" cur_ax.set_xlabel(\"time\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"x\", fontsize=LabelSize)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" tick.label.set_rotation(30)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
" \n",
" cur_ax = fig.add_subplot(2, 1, 2)\n",
" cur_ax.plot(times, y)\n",
" cur_ax.set_xlabel(\"time\", fontsize=LabelSize)\n",
" cur_ax.set_ylabel(\"f(x)\", fontsize=LabelSize)\n",
" for tick in cur_ax.xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" tick.label.set_rotation(30)\n",
" for tick in cur_ax.yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(LabelSize - font_gap)\n",
" \n",
" # fig.tight_layout()\n",
" # plt.subplots_adjust(wspace=0.05)#, hspace=0.4)\n",
" fig.savefig(save_path, dpi=dpi, bbox_inches=\"tight\", format=\"pdf\")\n",
" plt.close(\"all\")\n",
" # plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "shared-envelope",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Desktop is at: /Users/xuanyidong/Desktop\n"
]
}
],
"source": [
"# Visualization\n",
"home_dir = Path.home()\n",
"desktop_dir = home_dir / 'Desktop'\n",
"print('The Desktop is at: {:}'.format(desktop_dir))\n",
"visualize_syn(desktop_dir / 'tot-synthetic-v0.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "romantic-ordinance",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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notebooks/TOT/time-curve.py Normal file
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import os
import re
import sys
import torch
import qlib
import pprint
from collections import OrderedDict
import numpy as np
import pandas as pd
from pathlib import Path
# __file__ = os.path.dirname(os.path.realpath("__file__"))
note_dir = Path(__file__).parent.resolve()
root_dir = (Path(__file__).parent / ".." / "..").resolve()
lib_dir = (root_dir / "lib").resolve()
print("The root path: {:}".format(root_dir))
print("The library path: {:}".format(lib_dir))
assert lib_dir.exists(), "{:} does not exist".format(lib_dir)
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
import qlib
from qlib import config as qconfig
from qlib.workflow import R
qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=qconfig.REG_CN)
from utils.qlib_utils import QResult
def filter_finished(recorders):
returned_recorders = dict()
not_finished = 0
for key, recorder in recorders.items():
if recorder.status == "FINISHED":
returned_recorders[key] = recorder
else:
not_finished += 1
return returned_recorders, not_finished
def add_to_dict(xdict, timestamp, value):
date = timestamp.date().strftime("%Y-%m-%d")
if date in xdict:
raise ValueError("This date [{:}] is already in the dict".format(date))
xdict[date] = value
def query_info(save_dir, verbose, name_filter, key_map):
if isinstance(save_dir, list):
results = []
for x in save_dir:
x = query_info(x, verbose, name_filter, key_map)
results.extend(x)
return results
# Here, the save_dir must be a string
R.set_uri(str(save_dir))
experiments = R.list_experiments()
if verbose:
print("There are {:} experiments.".format(len(experiments)))
qresults = []
for idx, (key, experiment) in enumerate(experiments.items()):
if experiment.id == "0":
continue
if name_filter is not None and re.fullmatch(name_filter, experiment.name) is None:
continue
recorders = experiment.list_recorders()
recorders, not_finished = filter_finished(recorders)
if verbose:
print(
"====>>>> {:02d}/{:02d}-th experiment {:9s} has {:02d}/{:02d} finished recorders.".format(
idx + 1,
len(experiments),
experiment.name,
len(recorders),
len(recorders) + not_finished,
)
)
result = QResult(experiment.name)
for recorder_id, recorder in recorders.items():
file_names = ['results-train.pkl', 'results-valid.pkl', 'results-test.pkl']
date2IC = OrderedDict()
for file_name in file_names:
xtemp = recorder.load_object(file_name)['all-IC']
timestamps, values = xtemp.index.tolist(), xtemp.tolist()
for timestamp, value in zip(timestamps, values):
add_to_dict(date2IC, timestamp, value)
result.update(recorder.list_metrics(), key_map)
result.append_path(
os.path.join(recorder.uri, recorder.experiment_id, recorder.id)
)
result.append_date2ICs(date2IC)
if not len(result):
print("There are no valid recorders for {:}".format(experiment))
continue
else:
if verbose:
print(
"There are {:} valid recorders for {:}".format(
len(recorders), experiment.name
)
)
qresults.append(result)
return qresults
##
paths = [root_dir / 'outputs' / 'qlib-baselines-csi300']
paths = [path.resolve() for path in paths]
print(paths)
key_map = dict()
for xset in ("train", "valid", "test"):
key_map["{:}-mean-IC".format(xset)] = "IC ({:})".format(xset)
key_map["{:}-mean-ICIR".format(xset)] = "ICIR ({:})".format(xset)
qresults = query_info(paths, False, 'TSF-2x24-drop0_0s.*-.*-01', key_map)
print('Find {:} results'.format(len(qresults)))
times = []
for qresult in qresults:
times.append(qresult.name.split('0_0s')[-1])
print(times)
save_path = os.path.join(note_dir, 'temp-time-x.pth')
torch.save(qresults, save_path)
print(save_path)