mirror of
https://github.com/Doctorado-ML/Stree_datasets.git
synced 2025-08-15 15:36:01 +00:00
Refactor MySQL class and develop param_analysis
This commit is contained in:
@@ -7,26 +7,6 @@ title = "Best model results"
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lengths = (30, 9, 11, 11, 11, 11)
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def find_best(dataset, classifier):
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cursor = database.cursor(buffered=True)
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if classifier == "any":
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command = (
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f"select * from results r inner join reference e on "
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f"r.dataset=e.dataset where r.dataset='{dataset}' "
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)
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else:
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command = (
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f"select * from results r inner join reference e on "
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f"r.dataset=e.dataset where r.dataset='{dataset}' and classifier"
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f"='{classifier}'"
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)
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command += (
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" order by r.dataset, accuracy desc, classifier desc, type, date, time"
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)
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cursor.execute(command)
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return cursor.fetchone()
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def report_header_content(title):
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length = sum(lengths) + len(lengths) - 1
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output = "\n" + "*" * length + "\n"
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@@ -99,10 +79,10 @@ for item in [
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for dataset in dt:
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find_one = False
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line = {"dataset": color + dataset[0]}
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record = find_best(dataset[0], "any")
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record = dbh.find_best(dataset[0], "any")
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max_accuracy = 0.0 if record is None else record[5]
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for model in models:
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record = find_best(dataset[0], model)
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record = dbh.find_best(dataset[0], model)
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if record is None:
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line[model] = color + "-" * 9 + " "
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else:
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@@ -12,7 +12,7 @@ from .Utils import TextColor
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class MySQL:
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def __init__(self):
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self.server = None
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self._server = None
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def get_connection(self):
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config_db = dict()
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@@ -32,14 +32,35 @@ class MySQL:
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config_tunnel["ssh_address_or_host"] = make_tuple(
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config_tunnel["ssh_address_or_host"]
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)
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self.server = SSHTunnelForwarder(**config_tunnel)
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self.server.daemon_forward_servers = True
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self.server.start()
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config_db["port"] = self.server.local_bind_port
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return mysql.connector.connect(**config_db)
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self._server = SSHTunnelForwarder(**config_tunnel)
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self._server.daemon_forward_servers = True
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self._server.start()
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config_db["port"] = self._server.local_bind_port
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self._database = mysql.connector.connect(**config_db)
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return self._database
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def find_best(self, dataset, classifier="any"):
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cursor = self._database.cursor(buffered=True)
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if classifier == "any":
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command = (
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f"select * from results r inner join reference e on "
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f"r.dataset=e.dataset where r.dataset='{dataset}' "
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)
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else:
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command = (
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f"select * from results r inner join reference e on "
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f"r.dataset=e.dataset where r.dataset='{dataset}' and "
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f"classifier='{classifier}'"
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)
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command += (
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" order by r.dataset, accuracy desc, classifier desc, "
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"type, date, time"
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)
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cursor.execute(command)
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return cursor.fetchone()
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def close(self):
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self.server.close()
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self._server.close()
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class BD(ABC):
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@@ -1,395 +0,0 @@
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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sqlite3\n",
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"import mysql.connector\n",
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"from experimentation.Database import MySQL\n",
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"from experimentation.Sets import Datasets\n",
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"dbh = MySQL()\n",
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"database = dbh.get_connection()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"classifier = 'bagging'\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def find_best(dataset):\n",
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" cursor = database.cursor(buffered=True)\n",
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" if classifier == \"any\":\n",
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" command = (\n",
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" f\"select * from results r inner join reference e on \"\n",
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" f\"r.dataset=e.dataset where r.dataset='{dataset}' \"\n",
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" )\n",
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" else:\n",
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" command = (\n",
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" f\"select * from results r inner join reference e on \"\n",
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" f\"r.dataset=e.dataset where r.dataset='{dataset}' and classifier\"\n",
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" f\"='{classifier}'\"\n",
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" )\n",
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" command += (\n",
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" \" order by r.dataset, accuracy desc, classifier desc, type, date, time\"\n",
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" )\n",
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" cursor.execute(command)\n",
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" return cursor.fetchone()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def find_values(parameter, kernel_chosen):\n",
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" result = []\n",
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" for data in agg[kernel_chosen]:\n",
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" base_parameter = f\"base_estimator__{parameter}\"\n",
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" if parameter in data.keys():\n",
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" result.append(data[parameter])\n",
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" if base_parameter in data.keys():\n",
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" result.append(data[base_parameter])\n",
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" try:\n",
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" result_ordered = sorted(result)\n",
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" return result_ordered\n",
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" except TypeError:\n",
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" return result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Aggregating data ..................................................\n",
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"stree has 0 results\n",
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"adaBoost has 0 results\n",
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"bagging has 43 results\n",
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"odte has 0 results\n"
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]
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}
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],
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"source": [
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"dt = Datasets(False, False, 'tanveer')\n",
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"models = ['stree', 'adaBoost', 'bagging', 'odte']\n",
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"agg_models = {}\n",
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"for i in models:\n",
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" agg_models[i] = 0\n",
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"agg = {'linear': [], 'rbf': [], 'poly': []}\n",
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"print(\"Aggregating data .\", end='')\n",
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"for dataset in dt:\n",
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" result = find_best(dataset[0])\n",
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" print('.', end='')\n",
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" if result:\n",
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" agg_models[result[3]] += 1\n",
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" json_result = json.loads(result[8])\n",
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" key = json_result['kernel'] if 'kernel' in json_result.keys() else 'linear'\n",
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" agg[key].append(json_result)\n",
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"print('')\n",
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"for i in models:\n",
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" print(f\"{i:10} has {agg_models[i]:2} results\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Used kernel linear: 43 times\nUsed kernel poly: 0 times\nUsed kernel rbf: 0 times\n"
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]
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}
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],
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"source": [
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"print(\"Used kernel linear: \", len(agg['linear']), ' times')\n",
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"print(\"Used kernel poly: \", len(agg['poly']), ' times')\n",
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"print(\"Used kernel rbf: \", len(agg['rbf']), ' times')"
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]
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},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 7,
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||||
"metadata": {},
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||||
"outputs": [
|
||||
{
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||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[]"
|
||||
]
|
||||
},
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||||
"metadata": {},
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||||
"execution_count": 7
|
||||
}
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||||
],
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||||
"source": [
|
||||
"find_values('gamma', 'poly')"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[0.05,\n",
|
||||
" 0.05,\n",
|
||||
" 0.05,\n",
|
||||
" 0.05,\n",
|
||||
" 0.05,\n",
|
||||
" 0.05,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.2,\n",
|
||||
" 0.55,\n",
|
||||
" 0.55,\n",
|
||||
" 0.55,\n",
|
||||
" 1.0,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 7,\n",
|
||||
" 55,\n",
|
||||
" 55,\n",
|
||||
" 55,\n",
|
||||
" 55,\n",
|
||||
" 55,\n",
|
||||
" 55,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0,\n",
|
||||
" 10000.0]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"find_values('C', 'linear')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"find_values('C', 'poly')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 10
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"find_values('C', 'rbf')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.2,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.2,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.2,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.2,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" None,\n",
|
||||
" 0.6,\n",
|
||||
" 'auto',\n",
|
||||
" 0.6,\n",
|
||||
" 'auto']"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"find_values('max_features', 'linear')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dbh.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
]
|
||||
}
|
||||
156
param_analysis.py
Normal file
156
param_analysis.py
Normal file
@@ -0,0 +1,156 @@
|
||||
import json
|
||||
import argparse
|
||||
import collections
|
||||
from typing import Tuple
|
||||
from experimentation.Database import MySQL
|
||||
from experimentation.Sets import Datasets
|
||||
from experimentation.Utils import TextColor
|
||||
|
||||
kernel_names = ["linear", "rbf", "poly"]
|
||||
|
||||
|
||||
class Aggregation:
|
||||
def __init__(self, dbh):
|
||||
self._dbh = dbh
|
||||
self._report = {}
|
||||
self._model_names = ["stree", "adaBoost", "bagging", "odte"]
|
||||
self._kernel_names = kernel_names
|
||||
|
||||
def find_values(self, dataset, parameter):
|
||||
result = []
|
||||
for data in self._report[dataset]:
|
||||
base_parameter = f"base_estimator__{parameter}"
|
||||
if parameter in data.keys():
|
||||
result.append(data[parameter])
|
||||
if base_parameter in data.keys():
|
||||
result.append(data[base_parameter])
|
||||
try:
|
||||
result_ordered = sorted(result)
|
||||
return result_ordered
|
||||
except TypeError:
|
||||
return result
|
||||
|
||||
def load(self):
|
||||
dt = Datasets(False, False, "tanveer")
|
||||
print("Aggregating data of best results ...")
|
||||
for dataset in dt:
|
||||
if result := self._dbh.find_best(dataset[0]):
|
||||
accuracy = result[5]
|
||||
expected = result[10]
|
||||
model = result[3]
|
||||
json_result = json.loads(result[8])
|
||||
if "kernel" in json_result.keys():
|
||||
kernel = json_result["kernel"]
|
||||
elif "base_estimator__kernel" in json_result.keys():
|
||||
kernel = json_result["base_estimator__kernel"]
|
||||
else:
|
||||
kernel = "linear"
|
||||
best = accuracy > expected
|
||||
self._report[dataset[0]] = {
|
||||
"model": model,
|
||||
"kernel": kernel,
|
||||
"parameters": json_result,
|
||||
"best": best,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def report_header(title, lengths, fields, parameter):
|
||||
length = sum(lengths) + len(lengths) - 1
|
||||
output = "\n" + "*" * length + "\n"
|
||||
title = title + f" -- {parameter} parameter --"
|
||||
num = (length - len(title) - 2) // 2
|
||||
num2 = length - len(title) - 2 - 2 * num
|
||||
output += "*" + " " * num + title + " " * (num + num2) + "*\n"
|
||||
output += "*" * length + "\n\n"
|
||||
lines = ""
|
||||
for item, data in enumerate(fields):
|
||||
output += f"{fields[item]:{lengths[item]}} "
|
||||
lines += "=" * lengths[item] + " "
|
||||
output += f"\n{lines}"
|
||||
return output
|
||||
|
||||
def report(self, parameter):
|
||||
agg = {}
|
||||
agg_result = collections.OrderedDict()
|
||||
title = "Best Hyperparameters found for datasets"
|
||||
lengths = (32, 10, 7, 20)
|
||||
fields = (
|
||||
"Dataset",
|
||||
"Classifier",
|
||||
"Kernel",
|
||||
"Parameter Value",
|
||||
)
|
||||
print(Aggregation.report_header(title, lengths, fields, parameter))
|
||||
for i in self._kernel_names + self._model_names:
|
||||
agg[i] = {}
|
||||
agg[i]["total"] = 0
|
||||
agg[i]["better"] = 0
|
||||
agg[i]["worse"] = 0
|
||||
for dataset, data in self._report.items():
|
||||
kernel = data["kernel"]
|
||||
model = data["model"]
|
||||
if data["best"]:
|
||||
key = "better"
|
||||
sign = "+"
|
||||
else:
|
||||
key = "worse"
|
||||
sign = "-"
|
||||
base_parameter = f"base_estimator__{parameter}"
|
||||
result = ""
|
||||
if parameter in data["parameters"]:
|
||||
result = data["parameters"][parameter]
|
||||
try:
|
||||
agg_result[result] += 1
|
||||
except KeyError:
|
||||
agg_result[result] = 1
|
||||
elif base_parameter in data["parameters"]:
|
||||
result = data["parameters"][base_parameter]
|
||||
try:
|
||||
agg_result[result] += 1
|
||||
except KeyError:
|
||||
agg_result[result] = 1
|
||||
print(f"{sign} {dataset:30s} {model:10s} {kernel:7s} {result}")
|
||||
agg[kernel]["total"] += 1
|
||||
agg[kernel][key] += 1
|
||||
agg[model]["total"] += 1
|
||||
agg[model][key] += 1
|
||||
print(TextColor.BOLD, "Models", TextColor.ENDC)
|
||||
for i in self._model_names:
|
||||
print(
|
||||
f"{i:10} has {agg[i]['total']:2} results {agg[i]['better']:2} "
|
||||
f"better {agg[i]['worse']:2} worse"
|
||||
)
|
||||
print(TextColor.BOLD, "Kernels", TextColor.ENDC)
|
||||
for i in self._kernel_names:
|
||||
print(
|
||||
f"{i:10} has {agg[i]['total']:2} results {agg[i]['better']:2} "
|
||||
f"better {agg[i]['worse']:2} worse"
|
||||
)
|
||||
print(TextColor.BOLD, f"{parameter} Values:", TextColor.ENDC)
|
||||
try:
|
||||
max_len = f"{len(max(agg_result.keys(), key=len))}s"
|
||||
except TypeError:
|
||||
max_len = "10.2f"
|
||||
for key in sorted(agg_result):
|
||||
print(f"{key:{max_len}} -> {agg_result[key]:2d} times")
|
||||
|
||||
|
||||
def parse_arguments() -> Tuple[str, str, str, bool, bool]:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument(
|
||||
"-p",
|
||||
"--param",
|
||||
type=str,
|
||||
default="C",
|
||||
)
|
||||
args = ap.parse_args()
|
||||
return (args.param,)
|
||||
|
||||
|
||||
(param,) = parse_arguments()
|
||||
dbh = MySQL()
|
||||
dbh.get_connection()
|
||||
agg = Aggregation(dbh)
|
||||
agg.load()
|
||||
agg.report(param)
|
||||
dbh.close()
|
||||
@@ -32,26 +32,6 @@ def parse_arguments() -> Tuple[str, str, str, bool, bool]:
|
||||
)
|
||||
|
||||
|
||||
def find_best(dataset):
|
||||
cursor = database.cursor(buffered=True)
|
||||
if classifier == "any":
|
||||
command = (
|
||||
f"select * from results r inner join reference e on "
|
||||
f"r.dataset=e.dataset where r.dataset='{dataset}' "
|
||||
)
|
||||
else:
|
||||
command = (
|
||||
f"select * from results r inner join reference e on "
|
||||
f"r.dataset=e.dataset where r.dataset='{dataset}' and classifier"
|
||||
f"='{classifier}'"
|
||||
)
|
||||
command += (
|
||||
" order by r.dataset, accuracy desc, classifier desc, type, date, time"
|
||||
)
|
||||
cursor.execute(command)
|
||||
return cursor.fetchone()
|
||||
|
||||
|
||||
def report_header_content(title):
|
||||
length = sum(lengths) + len(lengths) - 1
|
||||
output = "\n" + "*" * length + "\n"
|
||||
@@ -144,7 +124,7 @@ for item in [
|
||||
] + models:
|
||||
agg[item] = 0
|
||||
for dataset in dt:
|
||||
record = find_best(dataset[0])
|
||||
record = dbh.find_best(dataset[0], classifier)
|
||||
if record is None:
|
||||
print(TextColor.FAIL + f"*No results found for {dataset[0]}")
|
||||
else:
|
||||
|
||||
Reference in New Issue
Block a user