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Lines changed: 417 additions & 485 deletions

‎imagedb/graphs.py‎

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‎imagedb/imagedb.pyproj‎

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<SchemaVersion>2.0</SchemaVersion>
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<ProjectGuid>965fc95c-7dbc-4efc-adab-5ed89451655a</ProjectGuid>
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<ProjectHome>.</ProjectHome>
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<StartupFile>oneoffwork.py</StartupFile>
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<StartupFile>graphs.py</StartupFile>
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<SearchPath>..\</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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<OutputPath>.</OutputPath>

‎jupyter/mv_graph_length_simple.ipynb‎

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‎jupyter/mv_graph_model.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 61,
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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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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"['adjid', 'Correction', 'Angle', '% MBE']"
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]
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},
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"execution_count": 58,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_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": 59,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"['n', 'Angle', 'style']"
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]
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},
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"execution_count": 59,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_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": 32,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"['Angle', 'bias_percent']"
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]
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},
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"execution_count": 32,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_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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},
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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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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th colspan=\"5\" halign=\"left\">% MAE</th>\n",
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" <th colspan=\"5\" halign=\"left\">% MBE</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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"</div>"
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],
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"text/plain": [
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" % MAE \\\n",
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" % MBE \\\n",
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" median percentile_25 percentile_75 mse_0 \n",
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"Correction \n",
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"Corrected -3.429877 -8.068872 0.260054 55.458272 \n",
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"Model corrected M=-0.5 95% CIs [-0.6, -0.3] "
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]
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},
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"execution_count": 45,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_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": 51,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['adjid', 'Correction', 'Angle', '% MAE']"
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"['adjid', 'Correction', 'Angle', '% MBE']"
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]
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},
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"execution_count": 51,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#same again but at 20 degrees\n",
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"#load data\n",
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"sql=\"select adjid ,'Model corrected' as Correction ,rotation as Angle ,bias_percent as '% MBE' from adjust_all where abs(rotation) < 21 union select adjid ,'Corrected' as Correction ,rotation as Angle ,all_corr_rot_adj2_mm_error_perc as '% MAE' from adjust_all where abs(rotation) < 21 union select adjid ,'Lens only' as Correction ,rotation as Angle ,mv_lens_correction_mm_error_perc as '% MAE' from adjust_all where abs(rotation) < 21 \"\n",
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"\n",
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"sql=\"select adjid ,'Model corrected' as Correction ,rotation as Angle ,bias_percent as '% MBE' from adjust_all where abs(rotation) < 21 union select adjid ,'Corrected' as Correction ,rotation as Angle ,all_corr_rot_adj2_mm_error_perc as '% MBE' from adjust_all where abs(rotation) < 21 union select adjid ,'Lens only' as Correction ,rotation as Angle ,mv_lens_correction_mm_error_perc as '% MBE' from adjust_all where abs(rotation) < 21 \"\n",
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"\n",
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"with mssql.Conn('imagedb', '(local)') as cnn:\n",
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" dfadj20 = pd.read_sql(sql, cnn)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
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"outputs": [],
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"source": [
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"dfadj20.groupby(['Correction']).agg({'% MBE':[np.mean, mse(0), ci]})"
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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": 64,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead tr th {\n",
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" text-align: left;\n",
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" }\n",
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"\n",
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" .dataframe thead tr:last-of-type th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th colspan=\"3\" halign=\"left\">% MBE</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>mse_0</th>\n",
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" <th>ci</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Correction</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>Corrected</th>\n",
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" <td>-2.049863</td>\n",
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" <td>28.576984</td>\n",
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" <td>M=-2.0 95% CIs [-2.2, -1.9]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Lens only</th>\n",
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" <td>-9.265658</td>\n",
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" <td>108.897709</td>\n",
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" <td>M=-9.3 95% CIs [-9.4, -9.1]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Model corrected</th>\n",
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" <td>-0.055286</td>\n",
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" <td>13.288200</td>\n",
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" <td>M=-0.1 95% CIs [-0.2, 0.1]</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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"pandas.core.frame.DataFrame"
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" % MBE \n",
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" mean mse_0 ci\n",
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"Correction \n",
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"Corrected -2.049863 28.576984 M=-2.0 95% CIs [-2.2, -1.9]\n",
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"Lens only -9.265658 108.897709 M=-9.3 95% CIs [-9.4, -9.1]\n",
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"Model corrected -0.055286 13.288200 M=-0.1 95% CIs [-0.2, 0.1]"
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]
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},
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"execution_count": 64,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" % MAE \\\n",
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" median percentile_25 percentile_75 mse_0 \n",
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"Correction \n",
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"Corrected -1.777259 -5.136181 1.044947 28.576984 \n",
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"Lens only -9.007796 -12.247619 -6.036656 108.897709 \n",
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"Model corrected -0.190457 -2.038379 1.811059 13.288200 \n",
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"\n",
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" \n",
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" ci \n",
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"Correction \n",
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"Corrected M=-2.0 95% CIs [-2.2, -1.9] \n",
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"Lens only M=-9.3 95% CIs [-9.4, -9.1] \n",
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"Model corrected M=-0.1 95% CIs [-0.2, 0.1] \n"
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]
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}
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],
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"source": []
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"source": [
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"dfadj20.groupby(['Correction']).agg({'% MBE':[np.mean, mse(0), ci]})"
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]
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},
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{
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"cell_type": "code",

‎jupyter/mv_graph_scale.ipynb‎

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‎jupyter/mv_sklearn_forest_outliers.ipynb‎

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"from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
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"\n",
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"\n",
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"from funclib.iolib import folder_open\n",
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"from funclib.baselib import pickle, unpickle\n",
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"from funclib.numericslib import roundx\n",
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"from dblib import mssql\n",
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"from plotlib.mplfuncs import FigWidthsInch\n",
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"from plotlib import qplot"
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"from funclib.iolib import folder_open #GGM library\n",
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"from funclib.baselib import pickle, unpickle #GGM library\n",
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"from funclib.numericslib import roundx #GGM library\n",
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"from dblib import mssql #GGM library\n",
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"from plotlib.mplfuncs import FigWidthsInch #GGM library\n",
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"from plotlib import qplot #GGM library"
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]
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},
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{
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}
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],
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"source": [
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"#This loads the data from SQL Server, load the CSV dataframe here\n",
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"sql=\"select tl_mm, lens_subj_triangle_est, abs(rotation) as rotationabs, rotation,accuracy ,hw_ratio, all_corr_rot_adj2_mm_error_perc as bias_percent, abs(all_corr_rot_adj2_mm_error_perc) as abs_bias_percent from v_mv_long_form where cnn = 'nas' and (transform like 'r%' or transform like 'none') and accuracy>0.5\"\n",
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"\n",
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"with mssql.Conn('imagedb', '(local)') as cnn:\n",

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