Repository: uf3/uf3 (language: python)

Issue:
Update setup.py
Required for pypi update. Simply update version from 'v0.3.2' to 'v0.4.0'


Discussion / hints:

Fix patch:
diff --git a/examples/tungsten_extxyz/radial_cutoff_hyperparameter_optimization_example.ipynb b/examples/tungsten_extxyz/radial_cutoff_hyperparameter_optimization_example.ipynb
new file mode 100644
index 00000000..7f61e526
--- /dev/null
+++ b/examples/tungsten_extxyz/radial_cutoff_hyperparameter_optimization_example.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0c2c7aaf",
+   "metadata": {},
+   "source": [
+    "# $\\text{UF}_{2,3}$ Demo: Outer Hyperparameter Optimization\n",
+    "\n",
+    "This jupyter notebook demonstrates how radial cutoffs (outer) hyperparameters can be optimized using a signle HDF5 feature file generated using a very large 2-body and 3-body cutoff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "28d65f4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from concurrent.futures import ProcessPoolExecutor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e53d3818",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "090deaa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from uf3.data import io\n",
+    "from uf3.data import geometry\n",
+    "from uf3.data import composition\n",
+    "from uf3.representation import bspline\n",
+    "from uf3.representation import process\n",
+    "from uf3.regression import least_squares\n",
+    "from uf3.regression.optimize import (get_bspline_config, \n",
+    "                               get_lower_cutoffs, \n",
+    "                               get_columns_to_drop_2b, \n",
+    "                               get_columns_to_drop_3b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45a0d5fd",
+   "metadata": {},
+   "source": [
+    "**Inputs**\n",
+    "- ```w-14.xyz``` Contains the training data. Available at [qmml.org](https://qmml.org/datasets.html)\n",
+    "- ```training_idx.txt``` Contains the training keys. Included for reproducibility purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "68f3e00b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#System Defination\n",
+    "element_list = ['W']\n",
+    "degree = 3\n",
+    "\n",
+    "chemical_system = composition.ChemicalSystem(element_list=element_list, degree=degree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "779ee1e9",
+   "metadata": {},
+   "source": [
+    "When creating HDF5 file for outer hyperparameter optimization, we **recommend** using the ```get_bspline_config``` function for constructing the ```bspline``` object. The current version is only tested for ```leading_trim=0``` and ```trailing_trim=3```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "30bb2706",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmin_2b = 0 #Min of radial cutoff for 2-body\n",
+    "rmin_3b = 1.6 #Min of radial cutoff for 2-body\n",
+    "\n",
+    "knot_spacing_2b = 0.5 #spacing between consecutive knots for 2-body\n",
+    "knot_spacing_3b = 0.8 #spacing between consecutive knots for 3-body\n",
+    "\n",
+    "rmax_2b = 8 #Cutoff 2-body\n",
+    "\n",
+    "rmax_3b = 5.6 #Cutoff 3-body. If A-B-C are the interacting bodies with A as the central atom. Then rmax_3b is\n",
+    "                #maximum possible distance between A-B (or A-C).\n",
+    "                #rmax_3b = Max dist(A,B) = Max dist(A,C) = Max dist(B,C)/2.\n",
+    "\n",
+    "bspline_config = get_bspline_config(chemical_system=chemical_system,\n",
+    "                                    rmin_2b=rmin_2b,\n",
+    "                                    rmin_3b=rmin_3b,\n",
+    "                                    rmax_2b=rmax_2b,\n",
+    "                                    rmax_3b=rmax_3b,\n",
+    "                                    knot_spacing_2b=knot_spacing_2b,\n",
+    "                                    knot_spacing_3b=knot_spacing_3b,\n",
+    "                                    leading_trim=0,trailing_trim=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9e944bf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('W',\n",
+       "  'W'): array([0. , 0. , 0. , 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5,\n",
+       "        5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8. , 8. , 8. ]),\n",
+       " ('W',\n",
+       "  'W',\n",
+       "  'W'): [array([1.6, 1.6, 1.6, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 5.6, 5.6, 5.6]), array([1.6, 1.6, 1.6, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 5.6, 5.6, 5.6]), array([ 1.6,  1.6,  1.6,  1.6,  2.4,  3.2,  4. ,  4.8,  5.6,  6.4,  7.2,\n",
+       "          8. ,  8.8,  9.6, 10.4, 11.2, 11.2, 11.2, 11.2])]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bspline_config.knots_map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8338c4cb",
+   "metadata": {},
+   "source": [
+    "# Load training keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a99d81f",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "57ce0db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_directory = os.getcwd()\n",
+    "data_filename = os.path.join(example_directory, \"w-14.xyz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0d69207c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of energies: 9693\n",
+      "Number of forces: 475578\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_coordinator = io.DataCoordinator()\n",
+    "data_coordinator.dataframe_from_trajectory(data_filename,\n",
+    "                                           prefix='dft')\n",
+    "df_data = data_coordinator.consolidate()\n",
+    "print(\"Number of energies:\", len(df_data))\n",
+    "print(\"Number of forces:\", int(np.sum(df_data[\"size\"]) * 3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33486d51",
+   "metadata": {},
+   "source": [
+    "# Compute energy and force features\n",
+    "This is the most expensive step. For demonstration purpose we will only featurize 50 entries. For reproducibility, ```sample_training_id.txt``` contain list of integers corresponding to entries to use for training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "84dda769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(os.path.join(example_directory, \"sample_training_id_radial_cutoff_opt.txt\"), \"r\") as f:\n",
+    "    training_50 = [int(idx) for idx in f.read().splitlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f964dc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"df_features.h5\"\n",
+    "table_template = \"features_{}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6eebc7eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_cores = 30\n",
+    "representation = process.BasisFeaturizer(bspline_config)\n",
+    "client = ProcessPoolExecutor(max_workers=n_cores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4c588a5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "representation.batched_to_hdf(filename,\n",
+    "                              df_data.iloc[training_50],\n",
+    "                              client,\n",
+    "                              n_jobs = n_cores,\n",
+    "                              batch_size=5,\n",
+    "                              progress='bar',\n",
+    "                              table_template=table_template)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "972ddabd",
+   "metadata": {},
+   "source": [
+    "Once the HDF5 feature file is genererated, use ```get_lower_cutoffs``` function to get all the possible cutoff values for ```rmax_2b``` and ```rmax_3b```."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "48222c24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_cutoffs = get_lower_cutoffs(original_bspline_config=bspline_config)\n",
+    "lower_rmax_2b, lower_rmax_3b = lower_cutoffs[\"lower_rmax_2b\"], lower_cutoffs[\"lower_rmax_3b\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "61bb27c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5,\n",
+       "       7. , 7.5, 8. ])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lower_rmax_2b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec9e0845",
+   "metadata": {},
+   "source": [
+    "**Remember** that if A-B-C are the interacting bodies with A as the central atom, then rmax_3b_poss is a list of maximum possible distances between A-B (or A-C). rmax_3b = Max dist(A,B) = Max dist(A,C) = Max dist(B,C)/2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "47b82e42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.4, 3.2, 4. , 4.8, 5.6])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lower_rmax_3b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f51aeeed",
+   "metadata": {},
+   "source": [
+    "Let's make a list of outer hyperparameter.\n",
+    "\n",
+    "Generally the cutoff of 3-body interaction (maximum side length of the triangle) is less than or euqal to 2-body cutoff.\n",
+    "\n",
+    "We will first make a ```meshgrid``` out of ```lower_rmax_2b``` and ```lower_rmax_3b```, and then apply the above condition.\n",
+    "\n",
+    "**Note** for the current implementation to work it is not necessary to follow the above condition "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a8390cd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lower_rmax_3b, lower_rmax_2b = np.meshgrid(lower_rmax_3b, lower_rmax_2b)\n",
+    "\n",
+    "lower_rmax_3b, lower_rmax_2b = lower_rmax_3b.flatten(), lower_rmax_2b.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "53814566",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[5.0, 2.4],\n",
+       " [5.5, 2.4],\n",
+       " [6.0, 2.4],\n",
+       " [6.5, 2.4],\n",
+       " [6.5, 3.2],\n",
+       " [7.0, 2.4],\n",
+       " [7.0, 3.2],\n",
+       " [7.5, 2.4],\n",
+       " [7.5, 3.2],\n",
+       " [8.0, 2.4],\n",
+       " [8.0, 3.2],\n",
+       " [8.0, 4.0]]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outer_hp = []\n",
+    "                                        \n",
+    "for i in range(lower_rmax_2b.shape[0]):\n",
+    "    if lower_rmax_2b[i]>=2*lower_rmax_3b[i]:\n",
+    "        outer_hp.append([lower_rmax_2b[i],lower_rmax_3b[i]])\n",
+    "outer_hp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8259164c",
+   "metadata": {},
+   "source": [
+    "As an example let's fit a model with ```rmax_2b = 7.0```, and ```rmax_3b = 3.0``` with no regularization and energy force weight =1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "402921e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('W',\n",
+       "  'W'): array([0. , 0. , 0. , 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5,\n",
+       "        5. , 5.5, 6. , 6.5, 6.5, 6.5, 6.5]),\n",
+       " ('W', 'W', 'W'): [array([1.6, 1.6, 1.6, 1.6, 2.4, 2.4, 2.4, 2.4]),\n",
+       "  array([1.6, 1.6, 1.6, 1.6, 2.4, 2.4, 2.4, 2.4]),\n",
+       "  array([1.6, 1.6, 1.6, 1.6, 2.4, 3.2, 4. , 4.8, 4.8, 4.8, 4.8])]}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rmax_2b = outer_hp[3][0]\n",
+    "rmax_3b = outer_hp[3][1]\n",
+    "\n",
+    "bspline_config_lower_cutoff = get_bspline_config(chemical_system=chemical_system,\n",
+    "                                                 rmin_2b=rmin_2b,\n",
+    "                                                 rmin_3b=rmin_3b,\n",
+    "                                                 rmax_2b=rmax_2b,\n",
+    "                                                 rmax_3b=rmax_3b,\n",
+    "                                                 knot_spacing_2b=knot_spacing_2b,\n",
+    "                                                 knot_spacing_3b=knot_spacing_3b,\n",
+    "                                                 leading_trim=0,trailing_trim=3)\n",
+    "\n",
+    "bspline_config_lower_cutoff.knots_map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "067964f7",
+   "metadata": {},
+   "source": [
+    "We need to know what columns to drop from the generated HDF5 file, so that the features correspond to cutoffs of ```rmax_2b = 7```, and ```rmax_3b = 3.0```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7e4e6d7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_drop_2b = get_columns_to_drop_2b(original_bspline_config=bspline_config,\n",
+    "                                         modify_2b_cutoff=rmax_2b,\n",
+    "                                         knot_spacing_2b=knot_spacing_2b)\n",
+    "\n",
+    "columns_to_drop_3b = get_columns_to_drop_3b(original_bspline_config=bspline_config,\n",
+    "                                            modify_3b_cutoff=rmax_3b,\n",
+    "                                            knot_spacing_3b=knot_spacing_3b)\n",
+    "\n",
+    "columns_to_drop = columns_to_drop_2b\n",
+    "columns_to_drop.extend(columns_to_drop_3b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4a56af8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regularizer = bspline_config_lower_cutoff.get_regularization_matrix(ridge_1b=0,\n",
+    "                                                                    ridge_2b=0.0,\n",
+    "                                                                    ridge_3b=0)\n",
+    "\n",
+    "model = least_squares.WeightedLinearModel(bspline_config_lower_cutoff, regularizer=regularizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "02d6989e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec6aeb0f3b22450c9b117b6f2fa65079",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.fit_from_file(filename=\"df_features.h5\",\n",
+    "                    subset=df_data.iloc[training_50].index,\n",
+    "                    weight=1,\n",
+    "                    batch_size=10,\n",
+    "                    energy_key=\"energy\", \n",
+    "                    progress=\"bar\",\n",
+    "                    drop_columns=columns_to_drop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c41140f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE (energy): 0.019\n",
+      "RMSE (forces): 0.514\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_e, p_e, y_f, p_f, rmse_e, rmse_f = model.batched_predict(filename=\"df_features.h5\",\n",
+    "                                                           keys=df_data.iloc[training_50].index,\n",
+    "                                                          drop_columns=columns_to_drop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb7aae91",
+   "metadata": {},
+   "source": [
+    "Next we will create 3-folds out of the 50 randomly generated structures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6baf1d71",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "df_folds = [i for i in np.array_split(df_data.iloc[training_50],3)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64a810f4",
+   "metadata": {},
+   "source": [
+    "Now we will fit models for all values in ```outer_hp``` and perform 3-fold CV.\n",
+    "\n",
+    "For demonstration we are not optimizing **inner hyperparameters** i.e regularizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b0609cf9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.0 2.4\n",
+      "RMSE (energy): 0.023\n",
+      "RMSE (forces): 0.516\n",
+      "RMSE (energy): 0.031\n",
+      "RMSE (forces): 0.445\n",
+      "RMSE (energy): 0.024\n",
+      "RMSE (forces): 0.475\n",
+      "RMSE (energy): 0.031\n",
+      "RMSE (forces): 0.625\n",
+      "RMSE (energy): 0.028\n",
+      "RMSE (forces): 0.468\n",
+      "RMSE (energy): 0.030\n",
+      "RMSE (forces): 0.411\n",
+      "5.5 2.4\n",
+      "RMSE (energy): 0.018\n",
+      "RMSE (forces): 0.551\n",
+      "RMSE (energy): 0.139\n",
+      "RMSE (forces): 0.482\n",
+      "RMSE (energy): 0.023\n",
+      "RMSE (forces): 0.514\n",
+      "RMSE (energy): 0.029\n",
+      "RMSE (forces): 0.682\n",
+      "RMSE (energy): 0.026\n",
+      "RMSE (forces): 0.539\n",
+      "RMSE (energy): 0.035\n",
+      "RMSE (forces): 0.496\n",
+      "6.0 2.4\n",
+      "RMSE (energy): 0.018\n",
+      "RMSE (forces): 0.554\n",
+      "RMSE (energy): 0.142\n",
+      "RMSE (forces): 0.483\n",
+      "RMSE (energy): 0.020\n",
+      "RMSE (forces): 0.609\n",
+      "RMSE (energy): 0.031\n",
+      "RMSE (forces): 0.953\n",
+      "RMSE (energy): 0.024\n",
+      "RMSE (forces): 0.523\n",
+      "RMSE (energy): 0.025\n",
+      "RMSE (forces): 0.467\n",
+      "6.5 2.4\n",
+      "RMSE (energy): 0.017\n",
+      "RMSE (forces): 0.649\n",
+      "RMSE (energy): 0.072\n",
+      "RMSE (forces): 0.481\n",
+      "RMSE (energy): 0.018\n",
+      "RMSE (forces): 0.447\n",
+      "RMSE (energy): 0.026\n",
+      "RMSE (forces): 0.517\n",
+      "RMSE (energy): 0.018\n",
+      "RMSE (forces): 0.562\n",
+      "RMSE (energy): 0.043\n",
+      "RMSE (forces): 0.393\n",
+      "6.5 3.2\n",
+      "RMSE (energy): 0.008\n",
+      "RMSE (forces): 3.015\n",
+      "RMSE (energy): 0.113\n",
+      "RMSE (forces): 1.053\n",
+      "RMSE (energy): 0.006\n",
+      "RMSE (forces): 2.517\n",
+      "RMSE (energy): 0.271\n",
+      "RMSE (forces): 10.508\n",
+      "RMSE (energy): 0.009\n",
+      "RMSE (forces): 3.743\n",
+      "RMSE (energy): 0.255\n",
+      "RMSE (forces): 10.376\n",
+      "7.0 2.4\n",
+      "RMSE (energy): 0.017\n",
+      "RMSE (forces): 0.604\n",
+      "RMSE (energy): 0.036\n",
+      "RMSE (forces): 0.472\n",
+      "RMSE (energy): 0.016\n",
+      "RMSE (forces): 0.471\n",
+      "RMSE (energy): 0.029\n",
+      "RMSE (forces): 0.671\n",
+      "RMSE (energy): 0.016\n",
+      "RMSE (forces): 0.531\n",
+      "RMSE (energy): 0.064\n",
+      "RMSE (forces): 0.380\n",
+      "7.0 3.2\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 3.196\n",
+      "RMSE (energy): 0.357\n",
+      "RMSE (forces): 2.284\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 2.465\n",
+      "RMSE (energy): 0.060\n",
+      "RMSE (forces): 6.651\n",
+      "RMSE (energy): 0.009\n",
+      "RMSE (forces): 4.283\n",
+      "RMSE (energy): 0.262\n",
+      "RMSE (forces): 10.450\n",
+      "7.5 2.4\n",
+      "RMSE (energy): 0.016\n",
+      "RMSE (forces): 0.513\n",
+      "RMSE (energy): 0.213\n",
+      "RMSE (forces): 0.556\n",
+      "RMSE (energy): 0.015\n",
+      "RMSE (forces): 0.467\n",
+      "RMSE (energy): 0.026\n",
+      "RMSE (forces): 0.672\n",
+      "RMSE (energy): 0.015\n",
+      "RMSE (forces): 0.453\n",
+      "RMSE (energy): 0.059\n",
+      "RMSE (forces): 0.382\n",
+      "7.5 3.2\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 3.238\n",
+      "RMSE (energy): 0.366\n",
+      "RMSE (forces): 2.319\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 2.352\n",
+      "RMSE (energy): 0.053\n",
+      "RMSE (forces): 6.448\n",
+      "RMSE (energy): 0.008\n",
+      "RMSE (forces): 3.602\n",
+      "RMSE (energy): 0.108\n",
+      "RMSE (forces): 3.285\n",
+      "8.0 2.4\n",
+      "RMSE (energy): 0.016\n",
+      "RMSE (forces): 0.492\n",
+      "RMSE (energy): 0.240\n",
+      "RMSE (forces): 0.563\n",
+      "RMSE (energy): 0.015\n",
+      "RMSE (forces): 0.443\n",
+      "RMSE (energy): 0.026\n",
+      "RMSE (forces): 0.629\n",
+      "RMSE (energy): 0.015\n",
+      "RMSE (forces): 0.455\n",
+      "RMSE (energy): 0.059\n",
+      "RMSE (forces): 0.383\n",
+      "8.0 3.2\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 3.171\n",
+      "RMSE (energy): 0.383\n",
+      "RMSE (forces): 2.301\n",
+      "RMSE (energy): 0.005\n",
+      "RMSE (forces): 2.320\n",
+      "RMSE (energy): 0.062\n",
+      "RMSE (forces): 6.122\n",
+      "RMSE (energy): 0.008\n",
+      "RMSE (forces): 3.628\n",
+      "RMSE (energy): 0.133\n",
+      "RMSE (forces): 4.377\n",
+      "8.0 4.0\n",
+      "RMSE (energy): 0.000\n",
+      "RMSE (forces): 3.229\n",
+      "RMSE (energy): 1.701\n",
+      "RMSE (forces): 2.955\n",
+      "RMSE (energy): 0.000\n",
+      "RMSE (forces): 1.366\n",
+      "RMSE (energy): 0.155\n",
+      "RMSE (forces): 4.016\n",
+      "RMSE (energy): 0.000\n",
+      "RMSE (forces): 1.650\n",
+      "RMSE (energy): 0.109\n",
+      "RMSE (forces): 1.945\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "cv_fold = 3\n",
+    "\n",
+    "for i in outer_hp:\n",
+    "    rmax_2b = i[0]\n",
+    "    rmax_3b = i[1]\n",
+    "    data = [rmax_2b,rmax_3b]\n",
+    "    print(rmax_2b,rmax_3b)\n",
+    "    bspline_config_lower_cutoff = get_bspline_config(chemical_system=chemical_system,\n",
+    "                                                     rmin_2b=rmin_2b,\n",
+    "                                                     rmin_3b=rmin_3b,\n",
+    "                                                     rmax_2b=rmax_2b,\n",
+    "                                                     rmax_3b=rmax_3b,\n",
+    "                                                     knot_spacing_2b=knot_spacing_2b,\n",
+    "                                                     knot_spacing_3b=knot_spacing_3b,\n",
+    "                                                     leading_trim=0,trailing_trim=3)\n",
+    "    \n",
+    "    columns_to_drop_2b = get_columns_to_drop_2b(original_bspline_config=bspline_config,\n",
+    "                                                modify_2b_cutoff=rmax_2b,\n",
+    "                                                knot_spacing_2b=knot_spacing_2b)\n",
+    "\n",
+    "    columns_to_drop_3b = get_columns_to_drop_3b(original_bspline_config=bspline_config,\n",
+    "                                                modify_3b_cutoff=rmax_3b,\n",
+    "                                                knot_spacing_3b=knot_spacing_3b)\n",
+    "    \n",
+    "    columns_to_drop = columns_to_drop_2b\n",
+    "    columns_to_drop.extend(columns_to_drop_3b)\n",
+    "    \n",
+    "    for hold_out_fold in range(0,cv_fold):\n",
+    "        df_train_data = [df_folds[j].copy() for j in range(len(df_folds)) if j!=hold_out_fold]\n",
+    "        df_valid_data = df_folds[hold_out_fold]\n",
+    "        \n",
+    "        df_train_data = pd.concat(df_train_data)\n",
+    "        \n",
+    "        training_keys = list(df_train_data.index)\n",
+    "        validation_keys = list(df_valid_data.index)\n",
+    "        \n",
+    "        regularizer = bspline_config_lower_cutoff.get_regularization_matrix(ridge_1b=0,\n",
+    "                                                                    ridge_2b=0.0,\n",
+    "                                                                    ridge_3b=0)\n",
+    "\n",
+    "        model = least_squares.WeightedLinearModel(bspline_config_lower_cutoff, regularizer=regularizer)\n",
+    "        \n",
+    "        model.fit_from_file(filename=\"df_features.h5\",\n",
+    "                            subset=training_keys,\n",
+    "                            weight=1,\n",
+    "                            batch_size=100,\n",
+    "                            energy_key=\"energy\", \n",
+    "                            progress=None,\n",
+    "                            drop_columns=columns_to_drop)\n",
+    "        \n",
+    "        yt_e, pt_e, yt_f, pt_f, rmset_e, rmset_f = model.batched_predict(filename=\"df_features.h5\",\n",
+    "                                                                         keys=training_keys,\n",
+    "                                                                         drop_columns=columns_to_drop)\n",
+    "        \n",
+    "        yv_e, pv_e, yv_f, pv_f, rmsev_e, rmsev_f = model.batched_predict(filename=\"df_features.h5\",\n",
+    "                                                                         keys=validation_keys,\n",
+    "                                                                         drop_columns=columns_to_drop)\n",
+    "        if hold_out_fold ==0:                                                   \n",
+    "            errors = np.array([rmset_e,rmset_f,rmsev_e,rmsev_f])     \n",
+    "        else:                                                                   \n",
+    "            errors = errors + np.array([rmset_e,rmset_f,rmsev_e,rmsev_f])\n",
+    "            \n",
+    "    errors = errors/cv_fold\n",
+    "    data.extend(list(errors))\n",
+    "    results.append(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f0ea4181",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rmax_2b</th>\n",
+       "      <th>rmax_3b</th>\n",
+       "      <th>training_error_energy</th>\n",
+       "      <th>traininig_error_force</th>\n",
+       "      <th>validation_error_energy</th>\n",
+       "      <th>validation_error_force</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>2.484099e-02</td>\n",
+       "      <td>0.486508</td>\n",
+       "      <td>0.030578</td>\n",
+       "      <td>0.493691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>2.224946e-02</td>\n",
+       "      <td>0.534941</td>\n",
+       "      <td>0.067506</td>\n",
+       "      <td>0.553128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>2.064678e-02</td>\n",
+       "      <td>0.561963</td>\n",
+       "      <td>0.065657</td>\n",
+       "      <td>0.634486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>1.762321e-02</td>\n",
+       "      <td>0.552773</td>\n",
+       "      <td>0.047054</td>\n",
+       "      <td>0.463623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>7.721066e-03</td>\n",
+       "      <td>3.091893</td>\n",
+       "      <td>0.213116</td>\n",
+       "      <td>7.312382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>1.628599e-02</td>\n",
+       "      <td>0.535050</td>\n",
+       "      <td>0.042964</td>\n",
+       "      <td>0.507692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>6.207416e-03</td>\n",
+       "      <td>3.314830</td>\n",
+       "      <td>0.226185</td>\n",
+       "      <td>6.461710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>1.546699e-02</td>\n",
+       "      <td>0.477571</td>\n",
+       "      <td>0.099653</td>\n",
+       "      <td>0.536610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>6.168754e-03</td>\n",
+       "      <td>3.064264</td>\n",
+       "      <td>0.175586</td>\n",
+       "      <td>4.017568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>1.535426e-02</td>\n",
+       "      <td>0.463413</td>\n",
+       "      <td>0.108616</td>\n",
+       "      <td>0.524774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>6.141637e-03</td>\n",
+       "      <td>3.039695</td>\n",
+       "      <td>0.192717</td>\n",
+       "      <td>4.266696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.660800e-08</td>\n",
+       "      <td>2.081573</td>\n",
+       "      <td>0.654772</td>\n",
+       "      <td>2.971912</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    rmax_2b  rmax_3b  training_error_energy  traininig_error_force  \\\n",
+       "0       5.0      2.4           2.484099e-02               0.486508   \n",
+       "1       5.5      2.4           2.224946e-02               0.534941   \n",
+       "2       6.0      2.4           2.064678e-02               0.561963   \n",
+       "3       6.5      2.4           1.762321e-02               0.552773   \n",
+       "4       6.5      3.2           7.721066e-03               3.091893   \n",
+       "5       7.0      2.4           1.628599e-02               0.535050   \n",
+       "6       7.0      3.2           6.207416e-03               3.314830   \n",
+       "7       7.5      2.4           1.546699e-02               0.477571   \n",
+       "8       7.5      3.2           6.168754e-03               3.064264   \n",
+       "9       8.0      2.4           1.535426e-02               0.463413   \n",
+       "10      8.0      3.2           6.141637e-03               3.039695   \n",
+       "11      8.0      4.0           1.660800e-08               2.081573   \n",
+       "\n",
+       "    validation_error_energy  validation_error_force  \n",
+       "0                  0.030578                0.493691  \n",
+       "1                  0.067506                0.553128  \n",
+       "2                  0.065657                0.634486  \n",
+       "3                  0.047054                0.463623  \n",
+       "4                  0.213116                7.312382  \n",
+       "5                  0.042964                0.507692  \n",
+       "6                  0.226185                6.461710  \n",
+       "7                  0.099653                0.536610  \n",
+       "8                  0.175586                4.017568  \n",
+       "9                  0.108616                0.524774  \n",
+       "10                 0.192717                4.266696  \n",
+       "11                 0.654772                2.971912  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_result = pd.DataFrame(results,columns=[\"rmax_2b\",\"rmax_3b\",\"training_error_energy\",\"traininig_error_force\",\n",
+    "                                         \"validation_error_energy\",\"validation_error_force\"])\n",
+    "df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "003aa71d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_result.sort_values(by=[\"rmax_2b\",\"rmax_3b\"],inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "77d9ccc1",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Model Complexity - 2B cutoff radius ($\\\\AA$)')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 800x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_result.sort_values(by=\"rmax_2b\",inplace=True)\n",
+    "plt.clf()\n",
+    "plt.rcParams.update({'font.size': 16})\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.subplots_adjust(left=0.15,bottom=0.07,right=0.99,top=0.99,hspace=0.05)\n",
+    "fig, axs = plt.subplots(2,1,figsize=(8,10),constrained_layout = True)\n",
+    "\n",
+    "energy_train_error = []\n",
+    "energy_valid_error = []\n",
+    "\n",
+    "force_train_error = []\n",
+    "force_valid_error = []\n",
+    "\n",
+    "cutoff_2b = df_result[\"rmax_2b\"].unique()\n",
+    "for i in cutoff_2b:\n",
+    "    df_temp = df_result.loc[df_result[\"rmax_2b\"]==i].copy()\n",
+    "    df_temp.sort_values(by=\"training_error_energy\",inplace=True)\n",
+    "    energy_train_error.append(df_temp[\"training_error_energy\"].to_numpy()[0])\n",
+    "    energy_valid_error.append(df_temp[\"validation_error_energy\"].to_numpy()[0])\n",
+    "    \n",
+    "    force_train_error.append(df_temp[\"traininig_error_force\"].to_numpy()[0])\n",
+    "    force_valid_error.append(df_temp[\"validation_error_force\"].to_numpy()[0])\n",
+    "\n",
+    "axs[0].plot(cutoff_2b,energy_train_error,'.-',color=\"tab:orange\",lw=2.4,ms=10,label=\"Training\")\n",
+    "axs[0].plot(cutoff_2b,energy_valid_error,'.-',color=\"tab:blue\",lw=2.4,ms=10,label=\"Validation\")\n",
+    "axs[0].set_ylabel(\"Eenrgy RMSE (eV/atom)\",fontsize=20)\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].plot(cutoff_2b,force_train_error,'.-',color=\"tab:orange\",lw=2.4,ms=10,label=\"Training\")\n",
+    "axs[1].plot(cutoff_2b,force_valid_error,'.-',color=\"tab:blue\",lw=2.4,ms=10,label=\"Validation\")\n",
+    "axs[1].set_ylabel(r\"Force RMSE (eV/$\\AA$)\",fontsize=20)\n",
+    "axs[1].legend()\n",
+    "axs[1].set_xlabel(r\"Model Complexity - 2B cutoff radius ($\\AA$)\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "01e0d0f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Model Complexity - 3B cutoff radius ($\\\\AA$)')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 800x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_result.sort_values(by=\"rmax_3b\",inplace=True)\n",
+    "plt.clf()\n",
+    "plt.rcParams.update({'font.size': 16})\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.subplots_adjust(left=0.15,bottom=0.07,right=0.99,top=0.99,hspace=0.05)\n",
+    "fig, axs = plt.subplots(2,1,figsize=(8,10),constrained_layout = True)\n",
+    "\n",
+    "energy_train_error = []\n",
+    "energy_valid_error = []\n",
+    "\n",
+    "force_train_error = []\n",
+    "force_valid_error = []\n",
+    "\n",
+    "cutoff_3b = df_result[\"rmax_3b\"].unique()\n",
+    "for i in cutoff_3b:\n",
+    "    df_temp = df_result.loc[df_result[\"rmax_3b\"]==i].copy()\n",
+    "    df_temp.sort_values(by=\"training_error_energy\",inplace=True)\n",
+    "    energy_train_error.append(df_temp[\"training_error_energy\"].to_numpy()[0])\n",
+    "    energy_valid_error.append(df_temp[\"validation_error_energy\"].to_numpy()[0])\n",
+    "    \n",
+    "    force_train_error.append(df_temp[\"traininig_error_force\"].to_numpy()[0])\n",
+    "    force_valid_error.append(df_temp[\"validation_error_force\"].to_numpy()[0])\n",
+    "\n",
+    "axs[0].plot(cutoff_3b,energy_train_error,'.-',color=\"tab:orange\",lw=2.4,ms=10,label=\"Training\")\n",
+    "axs[0].plot(cutoff_3b,energy_valid_error,'.-',color=\"tab:blue\",lw=2.4,ms=10,label=\"Validation\")\n",
+    "axs[0].set_ylabel(\"Eenrgy RMSE (eV/atom)\",fontsize=20)\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].plot(cutoff_3b,force_train_error,'.-',color=\"tab:orange\",lw=2.4,ms=10,label=\"Training\")\n",
+    "axs[1].plot(cutoff_3b,force_valid_error,'.-',color=\"tab:blue\",lw=2.4,ms=10,label=\"Validation\")\n",
+    "axs[1].set_ylabel(r\"Force RMSE (eV/$\\AA$)\",fontsize=20)\n",
+    "axs[1].legend()\n",
+    "axs[1].set_xlabel(r\"Model Complexity - 3B cutoff radius ($\\AA$)\",fontsize=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/tungsten_extxyz/sample_training_id_radial_cutoff_opt.txt b/examples/tungsten_extxyz/sample_training_id_radial_cutoff_opt.txt
new file mode 100644
index 00000000..d4d823e4
--- /dev/null
+++ b/examples/tungsten_extxyz/sample_training_id_radial_cutoff_opt.txt
@@ -0,0 +1,50 @@
+6179
+640
+7390
+6775
+9413
+4398
+2169
+1624
+3599
+4403
+2223
+8946
+9473
+2385
+6070
+6103
+6955
+6836
+399
+6423
+7290
+4381
+7815
+8386
+3378
+3690
+7784
+8424
+9421
+4041
+6335
+4749
+1841
+4313
+3583
+5296
+6502
+3025
+1753
+4887
+1485
+3759
+8506
+2904
+1540
+1639
+9343
+8175
+6623
+268
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 74cbeb5f..d7ffa810 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,7 @@
 if __name__ == "__main__":
     setuptools.setup(
         name='uf3',
-        version='0.3.2',
+        version='0.4.0',
         description='Ultra-Fast Force Fields for molecular dynamics',
         long_description=long_description,
         url='https://github.com/uf3/uf3',
@@ -43,4 +43,4 @@
                      'Operating System :: OS Independent',
                      'Topic :: Scientific/Engineering'],
         tests_require=test_requires,
-    )
\ No newline at end of file
+    )
diff --git a/uf3/regression/least_squares.py b/uf3/regression/least_squares.py
index 4fcdf96b..6f36879c 100644
--- a/uf3/regression/least_squares.py
+++ b/uf3/regression/least_squares.py
@@ -361,7 +361,8 @@ def fit_from_file(self,
                       batch_size=2500,
                       sample_weights: Dict = None,
                       energy_key="energy",
-                      progress: str = "bar"):
+                      progress: str = "bar",
+                      drop_columns: List[str] = None):
         """
         Accumulate inputs and outputs from batched parsing of HDF5 file
         and compute direct solution via LU decomposition.
@@ -376,6 +377,10 @@ def fit_from_file(self,
             sample_weights (dict):
             energy_key (str): column name for energies, default "energy".
             progress (str): style for progress indicators.
+            drop_columns (list): list of columns to drop. Used when modifying
+                the cutoffs of the feature vectors from HDF5 file. No internal
+                checks are performed to see if dropping provided columns produce
+                features of the intended cutoffs. Use with Caution.
         """
         if not os.path.isfile(filename):
             raise FileNotFoundError(filename)
@@ -391,6 +396,10 @@ def fit_from_file(self,
             keys = df.index.unique(level=0).intersection(subset)
             if len(keys) == 0:
                 continue
+
+            if drop_columns != None:
+                df.drop(columns=drop_columns,inplace=True)
+
             intermediates = self.gram_from_df(df,
                                               keys,
                                               e_variance=e_variance,
@@ -477,7 +486,8 @@ def batched_predict(self,
                         filename: str,
                         keys: List[str] = None,
                         table_names: List[str] = None,
-                        score: bool = True):
+                        score: bool = True,
+                        drop_columns: List[str] = None):
         """
         Extract inputs and outputs from HDF5 file and predict energies/forces.
 
@@ -494,13 +504,18 @@ def batched_predict(self,
             p_f (np.ndarray): prediction values for forces.
             rmse_e (np.ndarray): RMSE across energy predictions.
             rmse_e (np.ndarray): RMSE across force predictions.
+            drop_columns (list): list of columns to drop. Used when modifying
+                the cutoffs of the feature vectors from HDF5 file. No internal
+                checks are performed to see if dropping provided columns produce
+                features of the intended cutoffs. Use with Caution.
         """
         n_elements = len(self.bspline_config.element_list)
         y_e, p_e, y_f, p_f = batched_prediction(self,
                                                 filename,
                                                 table_names=table_names,
                                                 subset_keys=keys,
-                                                n_elements=n_elements)
+                                                n_elements=n_elements,
+                                                drop_columns=drop_columns)
         if score:
             rmse_e = rmse_metric(y_e, p_e)
             rmse_f = rmse_metric(y_f, p_f)
@@ -954,6 +969,7 @@ def batched_prediction(model: WeightedLinearModel,
                        filename: str,
                        table_names: Collection = None,
                        subset_keys: Collection = None,
+                       drop_columns: List[str] = None,
                        **kwargs):
     """
     Convenience function for optimization workflow. Read inputs/outputs
@@ -964,6 +980,10 @@ def batched_prediction(model: WeightedLinearModel,
         model (WeightedLinearModel): fitted model.
         table_names (list): list of table names to query from HDF5 file.
         subset_keys (list): list of keys to query from DataFrame.
+        drop_columns (list): list of columns to drop. Used when modifying
+            the cutoffs of the feature vectors from HDF5 file. No internal
+            checks are performed to see if dropping provided columns produce
+            features of the intended cutoffs. Use with Caution.
 
     Returns:
         y_e (np.ndarray): target values for energies.
@@ -979,6 +999,9 @@ def batched_prediction(model: WeightedLinearModel,
     y_f = []
     p_f = []
     for df in df_batches:
+        if drop_columns != None:
+            df.drop(columns=drop_columns,inplace=True)
+
         predictions = subset_prediction(df,
                                         model,
                                         subset_keys=subset_keys,
diff --git a/uf3/regression/optimize.py b/uf3/regression/optimize.py
new file mode 100644
index 00000000..151e93e8
--- /dev/null
+++ b/uf3/regression/optimize.py
@@ -0,0 +1,292 @@
+"""
+This module provides functions for optimizing the cutoffs of 2-body and 3-body
+terms. If a feature file exist with a higher cutoff, features with lower
+cutoffs can be constructed by droping appropriate columns. The functions
+provided only work when used with uniform knot spacing.
+"""
+
+import numpy as np
+from uf3.representation import bspline
+
+
+def get_bspline_config(
+    chemical_system,
+    rmin_2b: float,
+    rmin_3b: float,
+    rmax_2b: float,
+    rmax_3b: float,
+    knot_spacing_2b: float,
+    knot_spacing_3b: float,
+    leading_trim: float,
+    trailing_trim: float,
+):
+    """
+    Function for getting bspline_config object. We recommend using this function
+    to get bspling_config object for-
+        1. creating the HDF5 file with larger cutoffs
+        2. fitting model with lower cutoffs
+    The rmax_2b and rmax_3b values in the returned bspline_config object can be
+    slightly different depending on the knot_spacing
+
+    Args:
+        chemical_system: data.composition.ChemicalSystem
+        rmin_2b (float): Min of 2-body interaction
+        rmin_3b (float): Min of 3-body interaction
+        rmax_2b (float): Two-body cutoff
+        rmax_3b (float): Three-body cutoff. If the 3-body interaction is A-B-C,
+            then rmax_3b is the distance between A-B (or equivalently A-C) i.e
+            the first or second term in the 3-body rmax of bspline_config.
+        knot_spacing_2b (float): knot_spacing for 2-body interaction to create
+            the HDF5 file
+        knot_spacing_3b (float): knot_spacing for 3-body interaction to create
+            the HDF5 file
+        leading_trim (float): number of basis functions at leading edge
+            to suppress. Useful for ensuring smooth cutoffs.
+        trailing_trim (float): number of basis functions at trailing edge
+            to suppress. Useful for ensuring smooth cutoffs.
+
+    Returns:
+        bspline_config: bspline.BSplineBasis
+    """
+    if not (
+        np.isclose((rmax_2b - rmin_2b) % knot_spacing_2b, knot_spacing_2b)
+        or np.isclose((rmax_2b - rmin_2b) % knot_spacing_2b, 0)
+    ):
+
+        raise ValueError("Provided rmax_2b does not conatin integer number of\n\
+                knots, seperated by knot_spacing_2b")
+
+    if not (
+        np.isclose((rmax_3b - rmin_3b) % knot_spacing_3b, knot_spacing_3b)
+        or np.isclose((rmax_3b - rmin_3b) % knot_spacing_3b, 0)
+    ):
+
+        raise ValueError("Provided rmax_3b does not conatin integer number of\n\
+                knots, seperated by knot_spacing_3b")
+
+    if leading_trim != 0:
+        raise ValueError("Currrent version is only tested for leading_trim=0")
+
+    if trailing_trim != 3:
+        raise ValueError("Currrent version is only tested for trailing_trim=3")
+
+    rmax_3b_double = rmax_3b * 2
+    if not (
+        np.isclose(((rmax_3b_double - rmin_3b) % knot_spacing_3b), 0)
+        or np.isclose(((rmax_3b_double - rmin_3b) % knot_spacing_3b), knot_spacing_3b)
+    ):
+        raise ValueError(
+            "Provided (rmax_3b-rmin_3b) contains integer number of knots \n\
+                sperated by knot_spacing_3b, but rmax_3b_double does not. \n\
+                Consider changing rmin_3b, rmax_3b, knot_spacing_3b so that \n\
+                the following conditions are satisfied- \n\
+                --(rmax_3b - rmin_3b)/knot_spacing_3b == integer \n\
+                --(rmax_3b_double - rmin_3b)//knot_spacing_3b == integer, \n\
+                    where rmax_3b_double = 2*rmax_3b, calculated internally"
+        )
+
+    reso_2b = round((rmax_2b - rmin_2b) / knot_spacing_2b)
+    reso_3b = round((rmax_3b - rmin_3b) / knot_spacing_3b)
+
+    reso_3b_double = round((rmax_3b_double - rmin_3b) / knot_spacing_3b)
+
+    r_min_map = {i: rmin_2b for i in chemical_system.interactions_map[2]}
+    r_min_map.update(
+        {
+            i: [rmin_3b, rmin_3b, rmin_3b]
+            for i in chemical_system.interactions_map[3]
+        }
+    )
+
+    r_max_map = {i: rmax_2b for i in chemical_system.interactions_map[2]}
+    r_max_map.update(
+        {
+            i: [rmax_3b, rmax_3b, rmax_3b_double]
+            for i in chemical_system.interactions_map[3]
+        }
+    )
+
+    resolution_map = {i: reso_2b for i in chemical_system.interactions_map[2]}
+    resolution_map.update(
+        {
+            i: [reso_3b, reso_3b, reso_3b_double]
+            for i in chemical_system.interactions_map[3]
+        }
+    )
+
+    bspline_config = bspline.BSplineBasis(
+        chemical_system,
+        r_min_map=r_min_map,
+        r_max_map=r_max_map,
+        resolution_map=resolution_map,
+        trailing_trim=trailing_trim,
+        leading_trim=leading_trim,
+    )
+
+    return bspline_config
+
+
+def get_lower_cutoffs(original_bspline_config):
+    """
+    Function for getting cutoff values obtainable by droping columns of HDF5
+    file
+
+    Args:
+        original_bspline_config: bspline_config used to create the HDF5 feature
+            file. This file was produced with larger cutoff.
+
+    Returns:
+        Dict: {"lower_rmax_2b":lower_rmax_2b, "lower_rmax_3b":lower_rmax_3b}
+            lower_rmax_2b (list): List of possible 2-body cutoffs
+            lower_rmax_3b (list): List of possible 3-body cutoffs
+
+    """
+    interaction_2b = original_bspline_config.interactions_map[2][0]
+    interaction_3b = original_bspline_config.interactions_map[3][0]
+
+    lower_rmax_2b = original_bspline_config.knots_map[interaction_2b][4:-3]
+    lower_rmax_3b = original_bspline_config.knots_map[interaction_3b][0][4:-3]
+
+    for i in range(lower_rmax_2b.shape[0]):
+        if lower_rmax_2b[i] not in original_bspline_config.knots_map[interaction_2b]:
+            raise ValueError("Internal check failed-->2B!!")
+
+    for i in range(lower_rmax_3b.shape[0]):
+        if lower_rmax_3b[i] not in original_bspline_config.knots_map[interaction_3b][0]:
+            raise ValueError("Internal check failed-->3B_0!!")
+
+    for i in range(lower_rmax_3b.shape[0]):
+        if lower_rmax_3b[i] not in original_bspline_config.knots_map[interaction_3b][1]:
+            raise ValueError("Internal check failed-->3B_1!!")
+
+    return {"lower_rmax_2b": lower_rmax_2b, "lower_rmax_3b": lower_rmax_3b}
+
+
+def get_columns_to_drop_2b(
+    original_bspline_config, modify_2b_cutoff: float, knot_spacing_2b: float
+):
+    """
+    Function for getting appropriate 2-body feature columns to drop for fitting
+    to lower cutoffs
+
+    Args:
+        original_bspline_config: bspline_config used to create the HDF5 feature
+            file. This file was produced with larger cutoff.
+        modify_2b_cutoff (float): Intended 2-body cutoff
+        knot_spacing_2b (float): knot_spacing_2b used to create the HDF5 file
+    Returns:
+        columns_to_drop_2b (list): Should be passed to drop_columns argument of
+            fit_from_file
+    """
+    if original_bspline_config.leading_trim != 0:
+        raise ValueError("Currrent version is only tested for leading_trim=0")
+
+    if original_bspline_config.trailing_trim != 3:
+        raise ValueError("Currrent version is only tested for trailing_trim=3")
+
+    column_names = original_bspline_config.get_column_names()
+    interaction_partitions_num = original_bspline_config.get_interaction_partitions()[0]
+    interaction_partitions_posn = original_bspline_config.get_interaction_partitions()[1]
+
+    columns_to_drop_2b = []
+
+    for interaction in original_bspline_config.interactions_map[2]:
+        if modify_2b_cutoff not in original_bspline_config.knots_map[interaction]:
+            raise ValueError(
+                "Provided modify_2b_cutoff is not a knot in the %s interaction"
+                % (str(interaction))
+            )
+
+        num_columns_to_drop_2b = round(
+            (original_bspline_config.knots_map[interaction][-4] - modify_2b_cutoff)
+            / knot_spacing_2b
+        )
+
+        start_ind_2b = 1 + interaction_partitions_posn[interaction]
+        end_ind_2b = start_ind_2b + interaction_partitions_num[interaction]
+        columns_to_drop_2b.extend(
+            column_names[end_ind_2b - num_columns_to_drop_2b - 3:end_ind_2b - 3]
+        )
+    return columns_to_drop_2b
+
+
+def get_columns_to_drop_3b(
+    original_bspline_config, modify_3b_cutoff: float, knot_spacing_3b: float
+):
+    """
+    Function for getting appropriate 3-body feature columns to drop for fitting
+    to lower cutoffs
+
+    Args:
+        original_bspline_config: bspline.BSplineBasis used to create the HDF5
+            feature file. This file was produced with larger cutoff.
+        modify_3b_cutoff (float): Intended 3-body cutoff. If the 3-body interaction
+            is A-B-C, then modify_3b_cutoff is the maximum distance between A-B
+            (or A-C) i.e the first or second term in the 3-body rmax of
+            bspline_config.
+        knot_spacing_3b (float): knot_spacing_3b used to create the HDF5 file
+    Returns:
+        columns_to_drop_3b (list): Should be passed to drop_columns argument of
+            fit_from_file
+    """
+    if original_bspline_config.leading_trim != 0:
+        raise ValueError("Currrent version is only tested for leading_trim=0")
+
+    if original_bspline_config.trailing_trim != 3:
+        raise ValueError("Currrent version is only tested for trailing_trim=3")
+    column_names = original_bspline_config.get_column_names()
+    interaction_partitions_num = original_bspline_config.get_interaction_partitions()[0]
+    interaction_partitions_posn = original_bspline_config.get_interaction_partitions()[
+        1
+    ]
+
+    columns_to_drop_3b = []
+    for interaction in original_bspline_config.interactions_map[3]:
+        if modify_3b_cutoff not in original_bspline_config.knots_map[interaction][0]:
+            raise ValueError(
+                "Provided modify_3b_cutoff is not a knot in %s leg of %s interaction"
+                % (str((interaction[0], interaction[1])), str(interaction))
+            )
+
+        if modify_3b_cutoff not in original_bspline_config.knots_map[interaction][1]:
+            raise ValueError(
+                "Provided modify_3b_cutoff is not a knot in %s leg of %s interaction"
+                % (str((interaction[0], interaction[2])), str(interaction))
+            )
+
+        n_drop_3b = round(
+            (original_bspline_config.knots_map[interaction][0][-4] - modify_3b_cutoff)
+            / knot_spacing_3b
+        )
+        n_drop_3b_double = int(n_drop_3b * 2)
+        start_ind_3b = 1 + interaction_partitions_posn[interaction]
+        end_ind_3b = start_ind_3b + interaction_partitions_num[interaction]
+
+        l_space, m_space, n_space = original_bspline_config.knots_map[interaction]
+        L = len(l_space) - 4
+        M = len(m_space) - 4
+        N = len(n_space) - 4
+        grid_c = np.chararray((L, M, N))
+        grid_c[:] = "0"
+        grid_c = np.char.multiply(grid_c, 16)
+        grid_c.flat[original_bspline_config.template_mask[interaction]] = \
+            column_names[start_ind_3b:end_ind_3b]
+
+        grid_c = np.delete(grid_c, np.s_[grid_c.shape[2] - 3
+                        - n_drop_3b_double:grid_c.shape[2] - 3],
+                        axis=2)
+
+        grid_c = np.delete(grid_c, np.s_[grid_c.shape[1] - 3
+                    - n_drop_3b:grid_c.shape[1] - 3], axis=1)
+
+        grid_c = np.delete(grid_c, np.s_[grid_c.shape[0] - 3
+                    - n_drop_3b:grid_c.shape[0] - 3], axis=0)
+
+        columns_to_keep = grid_c[grid_c[:] != b"0000000000000000"].astype("<U16")
+
+        column_to_drop = np.setdiff1d(
+            column_names[start_ind_3b:end_ind_3b], columns_to_keep
+        )
+
+        columns_to_drop_3b.extend(column_to_drop)
+    return columns_to_drop_3b
diff --git a/uf3/representation/bspline.py b/uf3/representation/bspline.py
index e87ca5ca..b41958ac 100644
--- a/uf3/representation/bspline.py
+++ b/uf3/representation/bspline.py
@@ -917,7 +917,7 @@ def generate_uniform_knots(r_min: float,
     knots = np.linspace(r_min, r_max, n_intervals + 1)
     if sequence:
         knots = knot_sequence_from_points(knots)
-    return knots
+    return np.round(knots,10)
 
 
 def generate_inv_knots(r_min: float,


Test patch:
diff --git a/tests/test_optimize.py b/tests/test_optimize.py
new file mode 100644
index 00000000..8c489768
--- /dev/null
+++ b/tests/test_optimize.py
@@ -0,0 +1,166 @@
+import pytest
+import numpy as np
+import ase
+from uf3.data import composition
+from uf3.representation.process import *
+from uf3.regression import optimize
+
+@pytest.fixture()
+def Nb_Sn_chemistry():
+    element_list = ['Nb', 'Sn']
+    chemistry_config = composition.ChemicalSystem(element_list,degree=3)
+    yield chemistry_config
+
+@pytest.fixture()
+def config_1():
+    min_max_ks = {}
+    min_max_ks["rmin_2b"] = 0.01
+    min_max_ks["rmax_2b"] = 6.01
+    min_max_ks["rmin_3b"] = 0.8
+    min_max_ks["rmax_3b"] = 4
+    min_max_ks["knot_spacing_2b"] = 0.4
+    min_max_ks["knot_spacing_3b"] = 0.8
+    yield min_max_ks
+
+
+@pytest.fixture()
+def bspline_config_larger_cutoff(Nb_Sn_chemistry,config_1):
+    element_list = ['Nb', 'Sn']
+    chemistry_config = composition.ChemicalSystem(element_list,degree=3)
+    bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry,
+                                                    rmin_2b=config_1["rmin_2b"],
+                                                    rmin_3b=config_1["rmin_3b"],
+                                                    rmax_2b=config_1["rmax_2b"],
+                                                    rmax_3b=config_1["rmax_3b"],
+                                                    knot_spacing_2b=config_1["knot_spacing_2b"],
+                                                    knot_spacing_3b=config_1["knot_spacing_3b"],
+                                                    leading_trim=0,
+                                                    trailing_trim=3)
+    yield bspline_config
+
+@pytest.fixture()
+def config_2():
+    min_max_ks = {}
+    min_max_ks["rmin_2b"] = 0.1
+    min_max_ks["rmax_2b"] = 9.1
+    min_max_ks["rmin_3b"] = 0.9
+    min_max_ks["rmax_3b"] = 7.2
+    min_max_ks["knot_spacing_2b"] = 0.3
+    min_max_ks["knot_spacing_3b"] = 0.9
+    yield min_max_ks
+
+@pytest.fixture()
+def bspline_config_larger_cutoff_2(Nb_Sn_chemistry,config_2):
+    element_list = ['Nb', 'Sn']
+    chemistry_config = composition.ChemicalSystem(element_list,degree=3)
+    bspline_config = optimize.get_bspline_config(Nb_Sn_chemistry,
+                                                    rmin_2b=config_2["rmin_2b"],
+                                                    rmin_3b=config_2["rmin_3b"],
+                                                    rmax_2b=config_2["rmax_2b"],
+                                                    rmax_3b=config_2["rmax_3b"],
+                                                    knot_spacing_2b=config_2["knot_spacing_2b"],
+                                                    knot_spacing_3b=config_2["knot_spacing_3b"],
+                                                    leading_trim=0,
+                                                    trailing_trim=3)
+    yield bspline_config
+
+@pytest.fixture()
+def Nb3Sn_geom():
+    geom = ase.Atoms('Nb6Sn2',
+                    positions = [[0.000000000, 2.667305470, 1.333652735],
+                                [0.000000000, 2.667305470, 4.000958204],
+                                [1.333652735, 0.000000000, 2.667305470],
+                                [4.000958204, 0.000000000, 2.667305470],
+                                [2.667305470, 1.333652735, 0.000000000],
+                                [2.667305470, 4.000958204, 0.000000000],
+                                [0.000000000, 0.000000000, 0.000000000],
+                                [2.667305470, 2.667305470, 2.667305470]],
+                    pbc = True,
+                    cell = [[6.3346109390, 0.0000000000, 0.0000000000],
+                            [0.0000000000, 6.3346109390, 0.0000000000],
+                            [0.0000000000, 0.0000000000, 6.3346109390]])
+
+    yield geom
+
+
+class TestOptimize:
+
+    def test_drop_columns(self,bspline_config_larger_cutoff,Nb_Sn_chemistry, Nb3Sn_geom, config_1):
+        
+        cutoff_dict = optimize.get_lower_cutoffs(bspline_config_larger_cutoff)
+        
+        for i in range(len(cutoff_dict['lower_rmax_2b'])):
+            for j in range(len(cutoff_dict['lower_rmax_3b'])):
+                bspline_config_smaller_cutoff = optimize.get_bspline_config(Nb_Sn_chemistry,
+                                                                        rmin_2b=config_1["rmin_2b"],
+                                                                        rmin_3b=config_1["rmin_3b"],
+                                                                        rmax_2b=cutoff_dict['lower_rmax_2b'][i],
+                                                                        rmax_3b=cutoff_dict['lower_rmax_3b'][j],
+                                                                        knot_spacing_2b=config_1["knot_spacing_2b"],
+                                                                        knot_spacing_3b=config_1["knot_spacing_3b"],
+                                                                        leading_trim=0,
+                                                                        trailing_trim=3)
+
+                bspline_handler_larger_cutoff = BasisFeaturizer(bspline_config_larger_cutoff)
+                bspline_handler_smaller_cutoff = BasisFeaturizer(bspline_config_smaller_cutoff)
+        
+                columns_to_drop_2b = optimize.get_columns_to_drop_2b(original_bspline_config=bspline_config_larger_cutoff,
+                                                                    modify_2b_cutoff=cutoff_dict['lower_rmax_2b'][i],
+                                                                    knot_spacing_2b=config_1["knot_spacing_2b"])
+
+                columns_to_drop_3b = optimize.get_columns_to_drop_3b(original_bspline_config=bspline_config_larger_cutoff,
+                                                                    modify_3b_cutoff=cutoff_dict['lower_rmax_3b'][j],
+                                                                    knot_spacing_3b=config_1["knot_spacing_3b"])
+
+                columns_to_drop = columns_to_drop_2b + columns_to_drop_3b
+
+                assert len(bspline_handler_larger_cutoff.columns) - len(columns_to_drop) == len(bspline_handler_smaller_cutoff.columns)
+
+                energy_feature_larger_cutoff = bspline_handler_larger_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
+                energy_feature_smaller_cutoff = bspline_handler_smaller_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
+
+                index_to_drop = np.where(np.isin(bspline_handler_larger_cutoff.columns,columns_to_drop))[0]
+                energy_feature_from_larger_cutoff = np.delete(energy_feature_larger_cutoff['energy'],index_to_drop)
+
+                assert np.allclose(energy_feature_smaller_cutoff['energy'],energy_feature_from_larger_cutoff)
+
+
+    def test_drop_columns_2(self,bspline_config_larger_cutoff_2,Nb_Sn_chemistry, Nb3Sn_geom, config_2):
+        
+        cutoff_dict = optimize.get_lower_cutoffs(bspline_config_larger_cutoff_2)
+        for i in range(len(cutoff_dict['lower_rmax_2b'])):
+            for j in range(len(cutoff_dict['lower_rmax_3b'])):
+                bspline_config_smaller_cutoff = optimize.get_bspline_config(Nb_Sn_chemistry,
+                                                                        rmin_2b=config_2["rmin_2b"],
+                                                                        rmin_3b=config_2["rmin_3b"],
+                                                                        rmax_2b=cutoff_dict['lower_rmax_2b'][i],
+                                                                        rmax_3b=cutoff_dict['lower_rmax_3b'][j],
+                                                                        knot_spacing_2b=config_2["knot_spacing_2b"],
+                                                                        knot_spacing_3b=config_2["knot_spacing_3b"],
+                                                                        leading_trim=0,
+                                                                        trailing_trim=3)
+
+
+                bspline_handler_larger_cutoff = BasisFeaturizer(bspline_config_larger_cutoff_2)
+                bspline_handler_smaller_cutoff = BasisFeaturizer(bspline_config_smaller_cutoff)
+        
+                columns_to_drop_2b = optimize.get_columns_to_drop_2b(original_bspline_config=bspline_config_larger_cutoff_2,
+                                                                    modify_2b_cutoff=cutoff_dict['lower_rmax_2b'][i],
+                                                                    knot_spacing_2b=config_2["knot_spacing_2b"])
+
+                columns_to_drop_3b = optimize.get_columns_to_drop_3b(original_bspline_config=bspline_config_larger_cutoff_2,
+                                                                    modify_3b_cutoff=cutoff_dict['lower_rmax_3b'][j],
+                                                                    knot_spacing_3b=config_2["knot_spacing_3b"])
+
+                columns_to_drop = columns_to_drop_2b + columns_to_drop_3b
+
+                assert len(bspline_handler_larger_cutoff.columns) - len(columns_to_drop) == len(bspline_handler_smaller_cutoff.columns)
+
+                energy_feature_larger_cutoff = bspline_handler_larger_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
+                energy_feature_smaller_cutoff = bspline_handler_smaller_cutoff.evaluate_configuration(geom=Nb3Sn_geom,energy=-69.44185169)
+
+                index_to_drop = np.where(np.isin(bspline_handler_larger_cutoff.columns,columns_to_drop))[0]
+                energy_feature_from_larger_cutoff = np.delete(energy_feature_larger_cutoff['energy'],index_to_drop)
+
+                assert np.allclose(energy_feature_smaller_cutoff['energy'],energy_feature_from_larger_cutoff)
+